Journal articles: 'Pain network'

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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.

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Abstract Recent studies indicate that a significant reorganization of cerebral networks may occur in patients with chronic pain, but how immediate pain experience influences the organization of large-scale functional networks is not yet well characterized. To investigate this question, we used functional magnetic resonance imaging in 106 participants experiencing both noxious and innocuous heat. Painful stimulation caused network-level reorganization of cerebral connectivity that differed substantially from organization during innocuous stimulation and standard resting-state networks. Noxious stimuli increased somatosensory network connectivity with (a) frontoparietal networks involved in context representation, (b) “ventral attention network” regions involved in motivated action selection, and (c) basal ganglia and brainstem regions. This resulted in reduced “small-worldness,” modularity (fewer networks), and global network efficiency and in the emergence of an integrated “pain supersystem” (PS) whose activity predicted individual differences in pain sensitivity across 5 participant cohorts. Network hubs were reorganized (“hub disruption”) so that more hubs were localized in PS, and there was a shift from “connector” hubs linking disparate networks to “provincial” hubs connecting regions within PS. Our findings suggest that pain reorganizes the network structure of large-scale brain systems. These changes may prioritize responses to painful events and provide nociceptive systems privileged access to central control of cognition and action during pain.

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Meier, 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.

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Chronic musculoskeletal pain is a costly and prevalent condition that affects the lives of over 50 million individuals in the United States. Chronic pain leads to functional brain changes in those suffering from the condition. Not only does the primary pain network transform as the condition changes from acute to persistent pain, a state of hyper-connectivity also exists between the default mode, frontoparietal, and salience networks. Graph theory analysis has recently been used to investigate treatment-driven brain network changes. For example, current research suggests that Acceptance and Commitment Therapy (ACT) may reduce the chronic pain associated hyper-connectivity between the default mode, frontoparietal, and salience networks, as well as within the salience network. This study extended previous work by examining the associations between the three networks above and a meta-analytically derived pain network. Results indicate decreased connectivity within the pain network (including left putamen, right insula, left insula, and right thalamus) in addition to triple network connectivity changes after the four-week Acceptance and Commitment Therapy intervention.

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Cockett, Andrea. "Network on pain management." Paediatric Nursing 14, no. 4 (May 2002): 20. http://dx.doi.org/10.7748/paed.14.4.20.s23.

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Singavi, 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.

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Abstract Background:One-third of adults with sickle cell disease (SCD) have daily, chronic pain. Despite the high prevalence of chronic pain in adults with SCD, the mechanism of is not well defined. In other chronic pain disorders, functional MRI (fMRI) demonstrates a re-organization of the brain's connectivity, which may be maladaptive and contribute to the development of a chronic pain syndrome. We performed fMRI in adults with SCD as well as age-matched controls in order to test two hypotheses: 1) functional connectivity is different between adults with SCD and controls, and 2) differences in functional connectivity among adults with SCD are associated with a more severe pain phenotype. Methods:We performed resting-state fMRI in adults with SCD and age-matched controls. Functional connectivity was calculated using two approaches: 1) a seed-voxel approach with the seed being periaqueductal gray (PAG), an area of the brain known to inhibit pain sensation, and 2) an inter-network functional connectivity strength (FCS) analysis, in which seven brain functional networks were selected based on previous brain modularity analysis findings. To calculate the inter-network FCS between networks A and B, the summation of all functional connectivities between two networks are used. Thereafter, the networks that were significantly different in FCS between SCD and controls were used to determine the association between altered functional connectivity and pain phenotype within SCD subjects. Pain phenotype measurements in SCD subjects included a day-of-study pain score, a 15-day diary to document daily pain and opioid use, McGill pain and Pain DETECT questionnaires, and quantitative sensory testing in response to mechanical, cold, and heat stimuli. Statistical analyses were performed using FSL and Matlab software. Results: A total of 27 adults were examined, including 13 with SCD (9 HbSS, 4 HbSC) and 14 age-matched controls. Seed-based functional connectivity analyses revealed significantly decreased connectivity in SCD as compared to controls between PAG and the regions involved in pain, sensation, salience, emotion, learning, and memory (temporal gyrus, anterior/posterior insula, parahippocampal gyrus, fusiform gyrus, precunes, posterior cingulate gyrus, anterior cingulate, subcallosal gyrus, paracentral gyrus, inferior/superior parietal lobe, inferior frontal gyrus and superior temporal gyrus) (P<0.001, t-test with AlphaSim correction). Furthermore, inter-network analyses show significantly decreased FCS in SCD as compared to controls among networks involved in salience, emotion, learning, and memory (between the salience network and the striatum network, between the salience network and the temporal network, and within both the salience network and the hippocampus network) (P<0.001, t-test). When these inter-network differences in FCS between SCD subjects and controls were examined within SCD subjects to determine the association with clinical phenotype, significant associations were found with age (rs=0.63, P<0.024, Spearman correlation analysis), SCD genotype (SS vs SC) (r2=0.43, P<0.016, linear regression analysis), and number of diary days with pain score >5 (r2=0.5, P<0.011, linear regression analysis). Conclusions: In adults with SCD compared to controls, there were differences in inter-network FCS, including the salience, striatum, temporal, and hippocampus networks, which are crucial networks for salience, emotion, learning, and memory. When these inter-network FCS differences were examined within adults with SCD, significant associations were found with age, SCD genotype and number of pain days. Taken together, these data suggest that altered connectivity in the brain of adults with SCD contributes to the development of a chronic pain syndrome. These changes in functional connectivity on fMRI could be used as a biomarker to determine the efficacy of interventions targeted to decrease chronic pain. Disclosures Field: NKT Therapeutics: Research Funding; Astellas Pharmaceuticals: Research Funding.

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Mogil, 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.

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Hosomi, 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.

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Huang, 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.

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He, 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.

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Pain is considered an unpleasant perceptual experience associated with actual or potential somatic and visceral harm. Human subjects have different sensitivity to painful stimulation, which may be related to different painful response pattern. Excellent studies using functional magnetic resonance imaging (fMRI) have found the effect of the functional organization of white matter (WM) on the descending pain modulatory system, which suggests that WM function is feasible during pain modulation. In this study, 26 pain sensitive (PS) subjects and 27 pain insensitive (PIS) subjects were recruited based on cold pressor test. Then, all subjects underwent the cold bottle test (CBT) in normal (26 degrees temperature stimulating) and cold (8 degrees temperature stimulating) conditions during fMRI scan, respectively. WM functional networks were obtained using K-means clustering, and the functional connectivity (FC) was assessed among WM networks, as well as gray matter (GM)–WM networks. Through repeated measures ANOVA, decreased FC was observed between the GM–cerebellum network and the WM–superior temporal network, as well as the WM–sensorimotor network in the PS group under the cold condition, while this difference was not found in PIS group. Importantly, the changed FC was positively correlated with the state and trait anxiety scores, respectively. This study highlighted that the WM functional network might play an integral part in pain processing, and an altered FC may be related to the descending pain modulatory system.

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Seminowicz, 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.

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Experimental and clinical evidence indicates that pain can affect cognitive processes, but the cortical networks involved in pain-cognition interactions are unclear. In this study, we determined the effect of pain on the activity of cortical areas involved in cognition acting as a whole (i.e., a network). Subjects underwent functional magnetic resonance imaging (fMRI) while engaged in an attention-demanding cognitive task (multisource interference task) of varying difficulty and simultaneously receiving painful stimuli at varying intensities. The control (baseline) condition was simple finger tapping that had minimal cognitive demands and without pain. Functional connectivity analysis revealed a cortical network consisting of two anti-correlated parts: a task-negative part (precuneus/posterior cingulate cortex, medial frontal and inferior parietal/temporal) the activity of which correlated negatively with the cognitive task and positively with the control baseline, and a task-positive part (inferior frontal, superior parietal, premotor, and anterior insula cortices) the activity of which correlated positively with the cognitive task and negatively with the baseline. Independent components analysis revealed these opposing networks were operating at a low frequency (0.03–0.08 Hz). The functional connectivity of the task-positive network was increased by cognitive demand and by pain. We suggest this attention-specific network balances the needs of general self-referential and environmental awareness versus focused attention to salient information. We postulate that pain affects cognitive ability by its reliance on this common attention-specific network. These data provide evidence that pain can modulate a network presumed to be involved in focused attention, suggesting a mechanism for the interference of pain on cognitive ability by the consumption of attentional resources.

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Pereira, 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.

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Violence is a problem to be understood in an interdisciplinary way. This qualitative study aimed to understand the conception of women who experienced marital violence and structurally analyze their social support networks. Five women who reported their spouses to the Women’s Police Station (DECCM) and were being monitored by the Technical Team of the Women’s Emergency Support Service (SAPEM) were interviewed using a semi-structured questionnaire. A constructive-interpretive analysis was performed to identify the conceptions of experience of marital violence and through the Calgary Family Assessment Model (CFAM) it was possible to make an analysis and a graphic representation of the social support network for the women participating in this study. In general, it was observed that all women understand physical violence as actions that cause damage to the human anatomical and physiological structure. However, their conceptions are not limited to physical injury; they are also related to affective issues. For the participants, conjugal violence is not fragmented into “types of violence”, on the contrary, it occurs “agglutinated”, affecting the body and soul. Regarding the development of women’s social support network, they all have a family member as support – usually sons/daughters or mothers – and most of them count on the SAPEM technical team. The police station/police is also part of the network. Therefore, these tactics used in the social support network structure have different mechanisms by which the women reorganized their stories, electing some people and/or institutions, excluding others, highlighting this or that person and/or institution to make them agents for minimizing threatening behavior to themselves and their families. These people, when called in, seem to act either to curb violence and to strengthen the couple’s marital bonds or to break these bonds

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Petre, Bogdan, Philip Kragel, Lauren Y. Atlas, Stephan Geuter, Marieke Jepma, Leonie Koban, Anjali Krishnan, et al. "A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems." PLOS Biology 20, no. 5 (May 2, 2022): e3001620. http://dx.doi.org/10.1371/journal.pbio.3001620.

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Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. We compared models based on (a) a single most pain-predictive region or resting-state network; (b) pain-associated cortical–subcortical systems developed from prior literature (“multisystem models”); and (c) a model spanning the full brain. We estimated model accuracy using leave-one-study-out cross-validation (CV; 7 studies) and subsequently validated in 4 independent holdout studies. All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. Full brain models showed no predictive advantage over multisystem models. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains.

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Mansor, Muhammad Naufal, and Mohd Nazri Rejab. "Neural Network Performance Comparison in Infant Pain Expression Classifications." Applied Mechanics and Materials 475-476 (December 2013): 1104–9. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1104.

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Infant pain is a non-stationary made by infants in response to certain situations. This infant facial expression can be used to identify physical or psychology status of infant. The aim of this work is to compare the performance of features in infant pain classification. Fast Fourier Transform (FFT), and Singular value Decomposition (SVD) features are computed at different classifier. Two different case studies such as normal and pain are performed. Two different types of radial basis artificial neural networks namely, Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) are used to classify the infant pain. The results emphasized that the proposed features and classification algorithms can be used to aid the medical professionals for diagnosing pathological status of infant pain.

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Mano, Hiroaki, and Ben Seymour. "Pain: A Distributed Brain Information Network?" PLoS Biology 13, no. 1 (January 6, 2015): e1002037. http://dx.doi.org/10.1371/journal.pbio.1002037.

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Münzel, Thomas, and Gerd Heusch. "Chest Pain Unit Network in Germany." Journal of the American College of Cardiology 69, no. 19 (May 2017): 2459–60. http://dx.doi.org/10.1016/j.jacc.2017.03.562.

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Slomski, Anita. "Antidepressants May Affect Pain Neural Network." JAMA 322, no. 8 (August 27, 2019): 717. http://dx.doi.org/10.1001/jama.2019.12354.

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Fernández-Peña, Rosario, José Molina, and Oliver Valero. "Personal Network Analysis in the Study of Social Support: The Case of Chronic Pain." International Journal of Environmental Research and Public Health 15, no. 12 (November 29, 2018): 2695. http://dx.doi.org/10.3390/ijerph15122695.

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In the context of chronic illness, the individual’s social and relational environment plays a critical role as it can provide the informal support and care over time, beyond healthcare and social welfare institutions. Social Network Analysis represents an appropriate theoretical and methodological approach to study and understand social support since it provides measures of personal network structure, composition and functional content. The aim of this mixed method study is to present the usefulness of Personal Network Analysis to explore social support in the context of chronic pain. Personal and support network data of 30 people with chronic pain (20 alters for each ego, 600 relationships in total) were collected, obtaining measures of personal network structure and composition as well as information about social support characteristics. Also, semi-structured interviews with participants were conducted to identify the context of their experience of pain, their limitations as regards leading an autonomous life, their social support needs and other aspects concerning the effect of pain on their social and relational lives. This approach shows the importance of non-kin social support providers and the significant role of non-providers in the personal networks of people suffering chronic pain.

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Frankland, Paul W., and Cátia M. Teixeira. "A Pain in the ACC." Molecular Pain 1 (January 1, 2005): 1744–8069. http://dx.doi.org/10.1186/1744-8069-1-14.

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An emerging theme in systems neurobiology is that even simple forms of memory depend on activity in a broad network of cortical and subcortical brain regions. One key challenge is to understand how different components of these complex networks contribute to memory. In a new study in Molecular Pain, Tang and colleagues use a novel set of approaches to characterize the role of the anterior cingulate cortex (ACC) in the formation of Pavlovian fear memories.

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Gatti, Antonio, Rocco D. Mediati, Carlo Reale, Arturo Cuomo, Renato Vellucci, Gennaro Russo, Amedeo Costantini, et al. "Breakthrough Pain in Patients Referred to Pain Clinics: The Italian Pain Network Retrospective Study." Advances in Therapy 29, no. 5 (May 2012): 464–72. http://dx.doi.org/10.1007/s12325-012-0022-z.

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Thiam, Patrick, Hans A. Kestler, and Friedhelm Schwenker. "Two-Stream Attention Network for Pain Recognition from Video Sequences." Sensors 20, no. 3 (February 4, 2020): 839. http://dx.doi.org/10.3390/s20030839.

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Several approaches have been proposed for the analysis of pain-related facial expressions. These approaches range from common classification architectures based on a set of carefully designed handcrafted features, to deep neural networks characterised by an autonomous extraction of relevant facial descriptors and simultaneous optimisation of a classification architecture. In the current work, an end-to-end approach based on attention networks for the analysis and recognition of pain-related facial expressions is proposed. The method combines both spatial and temporal aspects of facial expressions through a weighted aggregation of attention-based neural networks’ outputs, based on sequences of Motion History Images (MHIs) and Optical Flow Images (OFIs). Each input stream is fed into a specific attention network consisting of a Convolutional Neural Network (CNN) coupled to a Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN). An attention mechanism generates a single weighted representation of each input stream (MHI sequence and OFI sequence), which is subsequently used to perform specific classification tasks. Simultaneously, a weighted aggregation of the classification scores specific to each input stream is performed to generate a final classification output. The assessment conducted on both the BioVid Heat Pain Database (Part A) and SenseEmotion Database points at the relevance of the proposed approach, as its classification performance is on par with state-of-the-art classification approaches proposed in the literature.

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Kolesar, Tiffany A., Elena Bilevicius, and Jennifer Kornelsen. "Salience, central executive, and sensorimotor network functional connectivity alterations in failed back surgery syndrome." Scandinavian Journal of Pain 16, no. 1 (July 1, 2017): 10–14. http://dx.doi.org/10.1016/j.sjpain.2017.01.008.

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AbstractObjectiveThis study examined the altered patterns of functional connectivity in task-positive resting state networks in failed back surgery syndrome (FBSS) patients compared to healthy controls using functional magnetic resonance imaging (fMRI). This work stems from a previous study in which alterations in the task-negative default mode network were investigated.DesignParticipants underwent a 7-minute resting state fMRI scan in which they lay still, with eyes closed, in the absence of a task.SettingScanning took place at the National Research Council’s 3 Tesla MRI magnet in Winnipeg, Canada.SubjectsFourteen patients with FBSS and age- and gender-matched controls participated in this study. Three patients were removed from the analyses due to image artefact (n = 1) and effective pain treatment (n = 2). Eleven patients (5 female, mean age 52.7 years) and their matched controls were included in the final analyses.MethodsResting state fMRI data were analyzed using an independent component analysis, yielding three resting state networks of interest: the salience network (SN), involved in detection of external stimuli, central executive network (CEN), involved in cognitions, and sensorimotor network (SeN), involved in sensory and motor integration. Analysis of Variance contrasts were performed for each network, comparing functional connectivity differences between FBSS patients and healthy controls.ResultsAlterations were observed in all three resting state networks, primarily relating to pain and its processing in the FBSS group. Specifically, compared to healthy controls, FBSS patients demonstrated increased functional connectivity in the anterior cingulate cortex within the SN, medial frontal gyrus in the CEN, and precentral gyrus within the SeN. FBSS patients also demonstrated decreased functional connectivity in the medial frontal gyrus in the SeN compared to healthy controls. Interestingly, we also observed internetwork functional connectivity in the SN and SeN.ConclusionsFBSS is associated with altered patterns of functional connectivity in the SN, CEN, and SeN. Taken together with our previous work, this reveals that a chronic pain condition can have a dramatic effect on the connectivity of multiple resting state networks.ImplicationsThese data suggest that a chronic pain condition—FBSS—is associated with disruptions to networks of functional connectivity in brain areas that are involved in numerous functions, including pain processing, sensation, and movement. It is possible that the alterations in these networks may contribute to other common chronic pain comorbidities, such as disrupted cognitions or anxiety. Previous research shows that during experimentally-induced pain, these networks can return to initial levels of functioning, indicating that these functional alterations are likely not permanent.

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Otti, Alexander, Harald Guendel, Peter Henningsen, Claus Zimmer, Afra Wohlschlaeger, and Michael Noll-Hussong. "Functional network connectivity of pain-related resting state networks in somatoform pain disorder: an exploratory fMRI study." Journal of Psychiatry & Neuroscience 38, no. 1 (January 1, 2013): 57–65. http://dx.doi.org/10.1503/jpn.110187.

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Kotecha, Gopal, Hiroaki Mano, Kenji Leibnitz, Aya Nakae, Valerie Voon, Wako Yoshida, Toshio Yanagida, Mitsuo Kawato, Maria Joao Rosa, and Ben Seymour. "A NEURAL BIOMARKER FOR CHRONIC PAIN BASED ON DECODED BRAIN NETWORKS." Journal of Neurology, Neurosurgery & Psychiatry 86, no. 11 (October 14, 2015): e4.108-e4. http://dx.doi.org/10.1136/jnnp-2015-312379.20.

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The lack of a biomarker for chronic pain remains an important impediment to clinical and translational pain research. The problem stems from the multiple parallel but subtle abnormalties thought to represent the chronic pain state, yielding the emerging view of chronic pain as a ‘network disorder’. This suggests analysis approaches that aim to identify distributed patterns of data (multivariate, machine learning methods) might offer the best opportunity to discover biomarkers. Here, we performed a multi-center functional brain imaging study to record state functional brain networks resting in 41 patients with chronic back pain and 33 healthy control subjects. We calculated with functional covariance matrix from 160 regions of interest, and used Sparse Multinomial Logistic Regression to classify subjects as patient or control using a leave-one-out cross validation. Diagnostic accuracy was 91.9%, with sensitivity and specificity 90.2% and 93.9% respectively. We then used graph theoretic measures to characterise the pattern of network differences between the groups, and showed that the chronic pain state was associated with disrupted network ‘assortativity’. These data provide evidence to support an accurate functional biomarker of chronic pain, and open the door to the development of translatable biomarkers using similar methodologies in animals.

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Farmer, Melissa A., Marwan N. Baliki, and A. Vania Apkarian. "A dynamic network perspective of chronic pain." Neuroscience Letters 520, no. 2 (June 2012): 197–203. http://dx.doi.org/10.1016/j.neulet.2012.05.001.

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Sheline, Yvette I., and Meichen Yu. "Linking antidepressant performance with pain network connectivity." Lancet Psychiatry 6, no. 8 (August 2019): 635–36. http://dx.doi.org/10.1016/s2215-0366(19)30250-0.

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Diers, Martin. "Neuroimaging the pain network – Implications for treatment." Best Practice & Research Clinical Rheumatology 33, no. 3 (June 2019): 101418. http://dx.doi.org/10.1016/j.berh.2019.05.003.

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Đuričanin, Jasminka, Marko Gašić, Jelena Veličković, and Nebojša Pavlović. "Advertising on Facebook social network." Bizinfo Blace 12, no. 2 (2021): 171–81. http://dx.doi.org/10.5937/bizinfo2102171d.

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The exponential growth of users on social networks around the world has led companies to explore effective ways of their presence on social networks. Accordingly, the trend of advertising as one of the most important forms of communication mix has changed and now companies are mainly focused on advertising on social networks. There are many social networks that companies can use for advertising, however, this paper points to the importance of advertising through the social network Facebook, given the fact that Facebook is the largest and most popular social network in the world and is the perfect marketing tool with a built-in advertising system, allows businesses to use each user's information for targeted advertising. Hence, Facebook is the most dominant social network for advertising, which every company should take into account when creating marketing strategies, in order to gain and maintain a competitive advantage and maximize business success. The paper presents secondary data that clearly indicate that Facebook is the social network that has the most potential for advertising, reaching and engaging consumers, which is supported by a discussion of the results of empirical research.

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Chong, Catherine D., Jennifer Nikolova, and Gina M. Dumkrieger. "Migraine and Posttraumatic Headache: Similarities and Differences in Brain Network Connectivity." Seminars in Neurology 42, no. 04 (August 2022): 441–48. http://dx.doi.org/10.1055/s-0042-1757929.

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AbstractPosttraumatic headache (PTH) is the most common symptom following mild traumatic brain injury (mTBI) (also known as concussion). Migraine and PTH have similar phenotypes, and a migraine-like phenotype is common in PTH. The similarities between both headache types are intriguing and challenge a better understanding of the pathophysiological commonalities involved in migraine and PTH due to mTBI. Here, we review the PTH resting-state functional connectivity literature and compare it to migraine to assess overlap and differences in brain network function between both headache types. Migraine and PTH due to mTBI have overlapping and disease-specific widespread alterations of static and dynamic functional networks involved in pain processing as well as dysfunctional network connections between frontal regions and areas of pain modulation and pain inhibition. Although the PTH functional network literature is still limited, there is some evidence that dysregulation of the top-down pain control system underlies both migraine and PTH. However, disease-specific differences in the functional circuitry are observed as well, which may reflect unique differences in brain architecture and pathophysiology underlying both headache disorders.

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Joo, So Young, Chang-hyun Park, Yoon Soo Cho, Cheong Hoon Seo, and Suk Hoon Ohn. "Plastic Changes in Pain and Motor Network Induced by Chronic Burn Pain." Journal of Clinical Medicine 10, no. 12 (June 11, 2021): 2592. http://dx.doi.org/10.3390/jcm10122592.

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Musculoskeletal diseases with chronic pain are difficult to control because of their association with both central as well as the peripheral nervous system. In burn patients, chronic pain is one of the major complications that cause persistent discomfort. The peripheral mechanisms of chronic pain by burn have been greatly revealed through studies, but the central mechanisms have not been identified. Our study aimed to characterize the cerebral plastic changes secondary to electrical burn (EB) and non-electrical burn (NEB) by measuring cerebral blood volume (CBV). Sixty patients, twenty with electrical burn (EB) and forty with non-electrical burn (NEB), having chronic pain after burn, along with twenty healthy controls, participated in the study. Voxel-wise comparisons of relative CBV maps were made among EB, NEB, and control groups over the entire brain volume. The CBV was measured as an increase and decrease in the pain and motor network including postcentral gyrus, frontal lobe, temporal lobe, and insula in the hemisphere associated with burned limbs in the whole burn group. In the EB group, CBV was decreased in the frontal and temporal lobes in the hemisphere associated with the burned side. In the NEB group, the CBV was measured as an increase or decrease in the pain and motor network in the postcentral gyrus, precentral gyrus, and frontal lobe of the hemisphere associated with the burn-affected side. Among EB and NEB groups, the CBV changes were not different. Our findings provide evidence of plastic changes in pain and motor network in patients with chronic pain by burn.

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Huang, Dong, Zhaoqiang Xia, Joshua Mwesigye, and Xiaoyi Feng. "Pain-attentive network: a deep spatio-temporal attention model for pain estimation." Multimedia Tools and Applications 79, no. 37-38 (August 2, 2020): 28329–54. http://dx.doi.org/10.1007/s11042-020-09397-1.

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Åkerblom, Sophia, Matti Cervin, Sean Perrin, Marcelo Rivano Fischer, Björn Gerdle, and Lance M. McCracken. "A Network Analysis of Clinical Variables in Chronic Pain: A Study from the Swedish Quality Registry for Pain Rehabilitation (SQRP)." Pain Medicine 22, no. 7 (March 11, 2021): 1591–602. http://dx.doi.org/10.1093/pm/pnaa473.

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Abstract Background Efforts to identify specific variables that impact most on outcomes from interdisciplinary pain rehabilitation are challenged by the complexity of chronic pain. Methods to manage this complexity are needed. The purpose of the study was to determine the network structure entailed in a set of self-reported variables, examine change, and look at potential predictors of outcome, from a network perspective. Methods In this study we apply network analysis to a large sample of people seeking interdisciplinary pain treatment (N = 2,241). Variables analyzed include pain intensity, pain interference, extent of pain, depression, anxiety, insomnia, and psychological variables from cognitive behavioral models of chronic pain. Results We found that Acceptance, Pain Interference, and Depression were key, “central,” variables in the pretreatment network. Interestingly, there were few changes in the overall network configuration following treatment, specifically with respect to which variables appear most central relative to each other. On the other hand, Catastrophizing, Depression, Anxiety, and Pain Interference each became less central over time. Changes in Life Control, Acceptance, and Anxiety were most strongly related to changes in the remainder of the network as a whole. Finally, no network differences were found between treatment responders and non-responders. Conclusions This study highlights potential future targets for pain treatment. Further application of a network approach to interdisciplinary pain rehabilitation data is recommended. Going forward, it may be better to next do this in a more comprehensive theoretically guided fashion, and ideographically, to detect unique individual differences in potential treatment processes.

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Jimeno, M. T., P. Collignon, Y. Chau, and B. Giusiano. "Utilization of a Neural Network in the Elaboration of an Evaluation Scale for Pain in Cerebral Palsy." Methods of Information in Medicine 34, no. 05 (September 1995): 498–502. http://dx.doi.org/10.1055/s-0038-1634630.

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Abstract:An interesting aspect of neural networks is shown in the elaboration of an evaluation scale for pain in cerebral palsy with severe mental retardation. Because of the diversity of cases, the number of items had to be limited in the final step of statistical validation. Classical analysis on prior data did not allow to decide whether the variability in results is more likely due to the type of disability (i. e., the possibility of pain expression) than to the actual presence of pain. A neural network was used to find implicit relations between the data, with the advantage of having total control on the variables’ status by applying variations in the network architecture. This allowed for the rapid identification more significant item combinations as a function of degree of relationship to pain in cerebral palsy.

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Mayer, E., B. Naliboff, L. Kilpatrick, J. Labus, C. Liu, C. Ashe-McNally, I. dos Santos, and K. Tillisch. "(305) The pain and interoception imaging network (PAIN) repository: towards big data approaches to chronic pain." Journal of Pain 16, no. 4 (April 2015): S52. http://dx.doi.org/10.1016/j.jpain.2015.01.223.

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Irwansyah, Irwansyah, Ade Davy Wiranata, Tupan Tri Muryono, and Agus Budiyantara. "SISTEM PAKAR DETEKSI KERUSAKAN JARINGAN LOCAL AREA NETWORK (LAN) MENGGUNAKAN METODE BECKWARD CHAINING BERBASIS WEB." Infotech: Journal of Technology Information 8, no. 2 (November 30, 2022): 135–42. http://dx.doi.org/10.37365/jti.v8i2.150.

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The form of computer network connection can be via cable or wireless such as fiber optic, microwave, wireless, or satellite. One type of computer network that is often used to connect personal computers and workstations in an office or an organization, company or factory for the use of shared resources is a local area network. The purpose of this research is to analyze, design and create an application that can detect damage to Local Area Network (LAN) networks. The research method used is backward chaining. The results of this study are applications that can detect damage to local area networks using the web-based backward chaining method. With this expert system application, it can speed up and make it easier to detect damage to Local Area Network networks.

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Reynolds, Christian A., and Zeljka Minic. "Chronic Pain-Associated Cardiovascular Disease: The Role of Sympathetic Nerve Activity." International Journal of Molecular Sciences 24, no. 6 (March 11, 2023): 5378. http://dx.doi.org/10.3390/ijms24065378.

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Chronic pain affects many people world-wide, and this number is continuously increasing. There is a clear link between chronic pain and the development of cardiovascular disease through activation of the sympathetic nervous system. The purpose of this review is to provide evidence from the literature that highlights the direct relationship between sympathetic nervous system dysfunction and chronic pain. We hypothesize that maladaptive changes within a common neural network regulating the sympathetic nervous system and pain perception contribute to sympathetic overactivation and cardiovascular disease in the setting of chronic pain. We review clinical evidence and highlight the basic neurocircuitry linking the sympathetic and nociceptive networks and the overlap between the neural networks controlling the two.

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Lee, UnCheol, Markus Müller, Gyu-Jeong Noh, ByungMoon Choi, and George A. Mashour. "Dissociable Network Properties of Anesthetic State Transitions." Anesthesiology 114, no. 4 (April 1, 2011): 872–81. http://dx.doi.org/10.1097/aln.0b013e31821102c9.

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Background It is still unknown whether anesthetic state transitions are continuous or binary. Mathematical graph theory is one method by which to assess whether brain networks change gradually or abruptly upon anesthetic induction and emergence. Methods Twenty healthy males were anesthetized with an induction dose of propofol, with continuous measurement of 21-channel electroencephalogram at baseline, during anesthesia, and during recovery. From these electroencephalographic data a "genuine network" was reconstructed based on the surrogate data method. The effects of topologic structure and connection strength on information transfer through the network were measured independently across different states. Results Loss of consciousness was consistently associated with a disruption of network topology. However, recovery of consciousness was associated with complex patterns of altered connection strength after the initial topologic structure had slowly recovered. In one group of subjects, there was a precipitous increase of connection strength that was associated with reduced variability of emergence time. Analysis of regional effects on brain networks demonstrated that the parietal network was significantly disrupted, whereas the frontal network was minimally affected. Conclusions By dissociating the effects of network structure and connection strength, both continuous and discrete elements of anesthetic state transitions were identified. The study also supports a critical role of parietal networks as a target of general anesthetics.

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Zinoviev, Dmitry. "The Pain of Complexity." Leonardo 49, no. 5 (October 2016): 450. http://dx.doi.org/10.1162/leon_a_01271.

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In the scholarly community, the concept of complexity is typically associated with science. However, the consumer view on complexity is different. To understand the conceptual structure of complexity, the author analyzed self-declared interests harvested from complexity-related blogs in LiveJournal. The author arranged the interests into a semantic network, based on their similarity of use. The network has a modular structure and consists of four clusters linked with four aspects of complexity: Science, Philosophy, Art and Soul. Apparently laypersons perceive complexity not only as a scientific phenomenon but also as an intricacy associated with creativity, search for wisdom, and a potentially painful soul search.

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Geuter, Stephan, Elizabeth A. Reynolds Losin, Mathieu Roy, Lauren Y. Atlas, Liane Schmidt, Anjali Krishnan, Leonie Koban, Tor D. Wager, and Martin A. Lindquist. "Multiple Brain Networks Mediating Stimulus–Pain Relationships in Humans." Cerebral Cortex 30, no. 7 (March 26, 2020): 4204–19. http://dx.doi.org/10.1093/cercor/bhaa048.

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Abstract The brain transforms nociceptive input into a complex pain experience comprised of sensory, affective, motivational, and cognitive components. However, it is still unclear how pain arises from nociceptive input and which brain networks coordinate to generate pain experiences. We introduce a new high-dimensional mediation analysis technique to estimate distributed, network-level patterns that formally mediate the relationship between stimulus intensity and pain. We applied the model to a large-scale analysis of functional magnetic resonance imaging data (N = 284), focusing on brain mediators of the relationship between noxious stimulus intensity and trial-to-trial variation in pain reports. We identify mediators in both traditional nociceptive pathways and in prefrontal, midbrain, striatal, and default-mode regions unrelated to nociception in standard analyses. The whole-brain mediators are specific for pain versus aversive sounds and are organized into five functional networks. Brain mediators predicted pain ratings better than previous brain measures, including the neurologic pain signature (Wager et al. 2013). Our results provide a broader view of the networks underlying pain experience, as well as novel brain targets for interventions.

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Xu, Lei, Taylor Bolt, Jason S. Nomi, Jialin Li, Xiaoxiao Zheng, Meina Fu, Keith M. Kendrick, Benjamin Becker, and Lucina Q. Uddin. "Inter-subject phase synchronization differentiates neural networks underlying physical pain empathy." Social Cognitive and Affective Neuroscience 15, no. 2 (February 2020): 225–33. http://dx.doi.org/10.1093/scan/nsaa025.

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Abstract Recent approaches for understanding the neural basis of pain empathy emphasize the dynamic construction of networks underlying this multifaceted social cognitive process. Inter-subject phase synchronization (ISPS) is an approach for exploratory analysis of task-fMRI data that reveals brain networks dynamically synchronized to task-features across participants. We applied ISPS to task-fMRI data assessing vicarious pain empathy in healthy participants (n = 238). The task employed physical (limb) and affective (face) painful and corresponding non-painful visual stimuli. ISPS revealed two distinct networks synchronized during physical pain observation, one encompassing anterior insula and midcingulate regions strongly engaged in (vicarious) pain and another encompassing parietal and inferior frontal regions associated with social cognitive processes which may modulate and support the physical pain empathic response. No robust network synchronization was observed for affective pain, possibly reflecting high inter-individual variation in response to socially transmitted pain experiences. ISPS also revealed networks related to task onset or general processing of physical (limb) or affective (face) stimuli which encompassed networks engaged in object manipulation or face processing, respectively. Together, the ISPS approach permits segregation of networks engaged in different psychological processes, providing additional insight into shared neural mechanisms of empathy for physical pain, but not affective pain, across individuals.

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Festa, Felice, Nicla Lopedote, Chiara Rotelli, Massimo Caulo, and Monica Macrì. "Correlation between Functional Magnetic Resonance and Symptomatologic Examination in Adult Patients with Myofascial Pain Syndrome of the Masticatory Muscles." Applied Sciences 13, no. 13 (July 6, 2023): 7934. http://dx.doi.org/10.3390/app13137934.

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Myofascial pain syndrome is the most common cause of TMD, characterised by trigger points of skeletal muscles in the masticatory region. Patients with myofascial pain suffer from orofacial pain and headaches. Parafunctional activity such as unconscious teeth clenching predisposes a higher possibility of developing myofascial pain. We report the results of a prospective study of 10 patients with a myofascial pain diagnosis related to TMD who underwent treatment with passive aligners and biofeedback exercise. All patients underwent pain assessment (visual analogic scale and muscular palpation test), measurement of masseters thickness with Dolphin Imaging Software, nuclear magnetic resonance of the temporomandibular joint, and functional nuclear magnetic resonance of the brain before and after gnathological treatment. The same patients underwent pain assessment (VAS and palpation test) for the entire duration of their treatment. This study aimed to assess if the results obtained with the therapy were repeatable using functional magnetic resonance imaging. This enabled us to correlate a subjective datum (pain) to an objective one (variation in the functional connectivity of the networks correlated to pain perception). According to the pain assessment, the treatment considerably reduced the pain in 9 out of 10 patients. Furthermore, the functional nuclear magnetic resonance of the brain showed similar modifications in the cerebral pain and default mode networks in these nine patients. The change in the masseter muscle dimensions was not correlated with the modification of pain. Statistical analysis was performed to evaluate the effects of treatment on VAS and trigger point stimulation and on the length and width of the masseter muscle. Linear regression analysis was used to assess a correlation between the modification of the masseter muscle dimension and the amendment of VAS. A paired t-test was used to evaluate statistically significant differences in the connectivity of brain areas of the DMN and the pain network. Our results suggest that the proper treatment of myofascial pain can reduce pain and consistently modify the functional activation of the cerebral pain and default mode networks. Overall, the treatment was repeatable because brain network changes were homogeneous in all patients and did not relate to the intracapsular TMJ condition but only to pain symptoms.

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Kiser, Amber C., Karen C. Schliep, Edgar Javier Hernandez, C. Matthew Peterson, Mark Yandell, and Karen Eilbeck. "An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis." PLOS ONE 19, no. 2 (February 21, 2024): e0297998. http://dx.doi.org/10.1371/journal.pone.0297998.

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Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

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Ozek, Burcu, Zhenyuan Lu, Srinivasan Radhakrishnan, and Sagar Kamarthi. "Uncertainty quantification in neural-network based pain intensity estimation." PLOS ONE 19, no. 8 (August 1, 2024): e0307970. http://dx.doi.org/10.1371/journal.pone.0307970.

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Improper pain management leads to severe physical or mental consequences, including suffering, a negative impact on quality of life, and an increased risk of opioid dependency. Assessing the presence and severity of pain is imperative to prevent such outcomes and determine the appropriate intervention. However, the evaluation of pain intensity is a challenging task because different individuals experience pain differently. To overcome this, many researchers in the field have employed machine learning models to evaluate pain intensity objectively using physiological signals. However, these efforts have primarily focused on pain point estimation, disregarding inherent uncertainty and variability in the data and model. A point estimate, which provides only partial information, is not sufficient for sound clinical decision-making. This study proposes a neural network-based method for objective pain interval estimation, and quantification of uncertainty. Our approach, which enables objective pain intensity estimation with desired confidence probabilities, affords clinicians a better understanding of a person’s pain intensity. We explored three distinct algorithms: the bootstrap method, lower and upper bound estimation (LossL) optimized by genetic algorithm, and modified lower and upper bound estimation (LossS) optimized by gradient descent algorithm. Our empirical results demonstrate that LossS outperforms the other two by providing narrower prediction intervals. For 50%, 75%, 85%, and 95% prediction interval coverage probability, LossS provides average interval widths that are 22.4%, 7.9%, 16.7%, and 9.1% narrower than those of LossL, and 19.3%, 21.1%, 23.6%, and 26.9% narrower than those of bootstrap. As LossS outperforms, we assessed its performance in three different model-building approaches: (1) a generalized approach using a single model for the entire population, (2) a personalized approach with separate models for each individual, and (3) a hybrid approach with models for clusters of individuals. Results demonstrate that the hybrid model-building approach provides the best performance.

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Darbon, Pascal. "Spinal cellular and network properties modulate pain perception." BIO Web of Conferences 6 (2016): 02001. http://dx.doi.org/10.1051/bioconf/20160602001.

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Forss, Nina, Tuukka T. Raij, Mika Seppä, and Riitta Hari. "Common cortical network for first and second pain." NeuroImage 24, no. 1 (January 2005): 132–42. http://dx.doi.org/10.1016/j.neuroimage.2004.09.032.

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von Leupoldt, Andreas, Tobias Sommer, Sarah Kegat, Hans Jörg Baumann, Hans Klose, Bernhard Dahme, and Christian Büchel. "Dyspnea and pain share emotion-related brain network." NeuroImage 48, no. 1 (October 2009): 200–206. http://dx.doi.org/10.1016/j.neuroimage.2009.06.015.

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Lelic, Dina, Søren Schou Olesen, Massimiliano Valeriani, and Asbjørn Mohr Drewes. "Brain source connectivity reveals the visceral pain network." NeuroImage 60, no. 1 (March 2012): 37–46. http://dx.doi.org/10.1016/j.neuroimage.2011.12.002.

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Damascelli, Matteo, Todd S. Woodward, Nicole Sanford, Hafsa B. Zahid, Ryan Lim, Alexander Scott, and John K. Kramer. "Multiple Functional Brain Networks Related to Pain Perception Revealed by fMRI." Neuroinformatics, June 8, 2021. http://dx.doi.org/10.1007/s12021-021-09527-6.

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AbstractThe rise of functional magnetic resonance imaging (fMRI) has led to a deeper understanding of cortical processing of pain. Central to these advances has been the identification and analysis of “functional networks”, often derived from groups of pre-selected pain regions. In this study our main objective was to identify functional brain networks related to pain perception by examining whole-brain activation, avoiding the need for a priori selection of regions. We applied a data-driven technique—Constrained Principal Component Analysis for fMRI (fMRI-CPCA)—that identifies networks without assuming their anatomical or temporal properties. Open-source fMRI data collected during a thermal pain task (33 healthy participants) were subjected to fMRI-CPCA for network extraction, and networks were associated with pain perception by modelling subjective pain ratings as a function of network activation intensities. Three functional networks emerged: a sensorimotor response network, a salience-mediated attention network, and the default-mode network. Together, these networks constituted a brain state that explained variability in pain perception, both within and between individuals, demonstrating the potential of data-driven, whole-brain functional network techniques for the analysis of pain imaging data.

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"The pain network (UK)." Acute Pain 1, no. 2 (March 1998): 53. http://dx.doi.org/10.1016/s1366-0071(98)80010-7.

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Payne, Laura A., Laura C. Seidman, Vitaly Napadow, Lisa D. Nickerson, and Poornima Kumar. "Functional connectivity associations with menstrual pain characteristics in adolescents: an investigation of the triple network model." Pain, July 18, 2024. http://dx.doi.org/10.1097/j.pain.0000000000003334.

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Abstract Menstrual pain is associated with deficits in central pain processing, yet neuroimaging studies to date have all been limited by focusing on group comparisons of adult women with vs without menstrual pain. This study aimed to investigate the role of the triple network model (TNM) of brain networks in adolescent girls with varied menstrual pain severity ratings. One hundred participants (ages 13-19 years) completed a 6-min resting state functional magnetic resonance imaging (fMRI) scan and rated menstrual pain severity, menstrual pain interference, and cumulative menstrual pain exposure. Imaging analyses included age and gynecological age (years since menarche) as covariates. Menstrual pain severity was positively associated with functional connectivity between the cingulo-opercular salience network (cSN) and the sensory processing regions, limbic regions, and insula, and was also positively associated with connectivity between the left central executive network (CEN) and posterior regions. Menstrual pain interference was positively associated with connectivity between the cSN and widespread brain areas. In addition, menstrual pain interference was positively associated with connectivity within the left CEN, whereas connectivity both within the right CEN and between the right CEN and cortical areas outside the network (including the insula) were negatively associated with menstrual pain interference. Cumulative menstrual pain exposure shared a strong negative association with connectivity between the default mode network and other widespread regions associated with large-scale brain networks. These findings support a key role for the involvement of TNM brain networks in menstrual pain characteristics and suggest that alterations in pain processing exist in adolescents with varying levels of menstrual pain.

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Jahn, Pauline, Bettina Deak, Astrid Mayr, Anne Stankewitz, Daniel Keeser, Ludovica Griffanti, Viktor Witkovsky, Stephanie Irving, and Enrico Schulz. "Intrinsic network activity reflects the ongoing experience of chronic pain." Scientific Reports 11, no. 1 (November 8, 2021). http://dx.doi.org/10.1038/s41598-021-01340-0.

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AbstractAnalyses of intrinsic network activity have been instrumental in revealing cortical processes that are altered in chronic pain patients. In a novel approach, we aimed to elucidate how intrinsic functional networks evolve in regard to the fluctuating intensity of the experience of chronic pain. In a longitudinal study with 156 fMRI sessions, 20 chronic back pain patients and 20 chronic migraine patients were asked to continuously rate the intensity of their endogenous pain. We investigated the relationship between the fluctuation of intrinsic network activity with the time course of subjective pain ratings. For chronic back pain, we found increased cortical network activity for the salience network and a local pontine network, as well as decreased network activity in the anterior and posterior default mode network for higher pain intensities. Higher pain intensities in chronic migraine were accompanied with lower activity in a prefrontal cortical network. By taking the perspective of the individual, we focused on the variability of the subjective perception of pain, which include phases of relatively low pain and phases of relatively high pain. The present design of the assessment of ongoing endogenous pain can be a powerful and promising tool to assess the signature of a patient’s endogenous pain encoding.

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De Ridder, Dirk, Sven Vanneste, Mark Smith, and Divya Adhia. "Pain and the Triple Network Model." Frontiers in Neurology 13 (March 7, 2022). http://dx.doi.org/10.3389/fneur.2022.757241.

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Acute pain is a physiological response that causes an unpleasant sensory and emotional experience in the presence of actual or potential tissue injury. Anatomically and symptomatically, chronic pathological pain can be divided into three distinct but interconnected pathways, a lateral “painfulness” pathway, a medial “suffering” pathway and a descending pain inhibitory circuit. Pain (fullness) can exist without suffering and suffering can exist without pain (fullness). The triple network model is offering a generic unifying framework that may be used to understand a variety of neuropsychiatric illnesses. It claims that brain disorders are caused by aberrant interactions within and between three cardinal brain networks: the self-representational default mode network, the behavioral relevance encoding salience network and the goal oriented central executive network. A painful stimulus usually leads to a negative cognitive, emotional, and autonomic response, phenomenologically expressed as pain related suffering, processed by the medial pathway. This anatomically overlaps with the salience network, which encodes behavioral relevance of the painful stimuli and the central sympathetic control network. When pain lasts longer than the healing time and becomes chronic, the pain- associated somatosensory cortex activity may become functionally connected to the self-representational default mode network, i.e., it becomes an intrinsic part of the self-percept. This is most likely an evolutionary adaptation to save energy, by separating pain from sympathetic energy-consuming action. By interacting with the frontoparietal central executive network, this can eventually lead to functional impairment. In conclusion, the three well-known pain pathways can be combined into the triple network model explaining the whole range of pain related co-morbidities. This paves the path for the creation of new customized and personalized treatment methods.

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Journal articles on the topic 'Pain network'

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