Chronic generalized pain disrupts whole brain functional connectivity in mice

Nasseef, Taufiq; Ma, Weiya; Singh, Jai Puneet; Dozono, Naoki; Lançon, Kevin; Séguéla, Philippe; Darcq, Emmanuel; Ueda, Hiroshi; Kieffer, Brigitte L.

doi:10.1007/s11682-020-00438-9

Cited by 10 publications

(12 citation statements)

References 48 publications

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“…Surprisingly, a COVID-19 mouse is also already available for research [98]. New insights on COVID-19 might be found through rodent behavioral experiment [99][100][101][102][103], brain imaging [99][100][101][102][103][104], advanced computational image analysis [100,103,105] and cellular level gene expression [106]. Such imaging and gene data can be helpful for mathematical modeling and data analysis as to investigate long-term future effects, to make predictions and prevent COVID-19 transmission.…”

Section: Discussionmentioning

confidence: 99%

Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics

et al. 2022

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The entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper summarizes all the available mathematical models that have been used in predicting the transmission of COVID-19. A total of nine mathematical models have been thoroughly reviewed and characterized in this work, so as to understand the intrinsic properties of each model in predicting disease transmission dynamics. The application of these nine models in predicting COVID-19 transmission dynamics is presented with a case study, along with detailed comparisons of these models. Toward the end of the paper, key behavioral properties of each model, relevant challenges and future directions are discussed.

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Section: Discussionmentioning

confidence: 99%

Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics

et al. 2022

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“…There was a significant positive correlation with self-assessment anxiety [ 26 ]. There are also a large number of studies suggesting that chronic pain can also cause abnormal brain function in patients [ 27 – 30 ]. In this study, there were also some patients with abnormal brain function in the control group, however, the signs were not very obvious.…”

Section: Discussionmentioning

confidence: 99%

Image Features of Resting‐State Functional Magnetic Resonance Imaging in Evaluating Poor Emotion and Sleep Quality in Patients with Chronic Pain under Artificial Intelligence Algorithm

Yang

Bie

Wang

et al. 2022

Contrast Media & Molecular Imaging

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The balanced iterative reducing and clustering using hierarchies (BIRCH) method was adopted to optimize the results of the resting-state functional magnetic resonance imaging (RS-fMRI) to analyze the changes in the brain function of patients with chronic pain accompanied by poor emotion or abnormal sleep quality in this study, so as to provide data support for the prevention and treatment of clinical chronic pain with poor emotion or sleep quality. 159 patients with chronic pain who visited the hospital were selected as the research objects, and they were grouped according to the presence or absence of abnormalities in emotion and sleep. The patients without poor emotion and sleep quality were set as the control group (60 cases), and the patients with the above symptoms were defined in the observation group (90 cases). The brain function was detected by RS-fMRI technology based on the BIRCH algorithm. The results showed that the rand index (RI), adjustment of RI (ARI), and Fowlkes–Mallows index (FMI) results in the k-means, flow cytometry (FCM), and BIRCH algorithms were 0.82, 0.71, and 0.88, respectively. The scores of Hamilton Depression Scale (HAHD), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Index (PSQI) were 7.26 ± 3.95, 7.94 ± 3.15, and 8.03 ± 4.67 in the observation group and 4.03 ± 1.95, 5.13 ± 2.35, and 4.43 ± 2.07 in the control group; the higher proportion of RS-fMRI was with abnormal brain signal connections. A score of 7 or more meant that the number of brain abnormalities was more than 90% and that of less than 7 was less than 40%, showing a statistically obvious difference in contrast P < 0.05 . Therefore, the BIRCH clustering algorithm showed reliable value in the optimization of RS-fMRI images, and RS-fMRI showed high application value in evaluating the emotion and sleep quality of patients with chronic pain.

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“… 252 Therefore, and, not surprisingly, the RSC, as a major node in the DMN, undergoes functional connectivity alterations in chronic pain models. 253 RSC functional connectivity with the PFC and subiculum have also been shown to be increased in chronic pain. 252 These studies set the stage for the RSC as a brain region also involved in non-hippocampal functions.…”

Section: Pre-frontal Cortex and Non-hippocampal-dependent Behaviormentioning

confidence: 98%

Peering into the Brain through the Retrosplenial Cortex to Assess Cognitive Function of the Injured Brain

Motanis

Khorasani

Giza

et al. 2021

Neurotrauma Reports

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The retrosplenial cortex (RSC) is a posterior cortical area that has been drawing increasing interest in recent years, with a growing number of studies studying its contribution to cognitive and sensory functions. From an anatomical perspective, it has been established that the RSC is extensively and often reciprocally connected with the hippocampus, neocortex, and many midbrain regions. Functionally, the RSC is an important hub of the default-mode network. This endowment, with vast anatomical and functional connections, positions the RSC to play an important role in episodic memory, spatial and contextual learning, sensory-cognitive activities, and multi-modal sensory information processing and integration. Additionally, RSC dysfunction has been reported in cases of cognitive decline, particularly in Alzheimer's disease and stroke. We review the literature to examine whether the RSC can act as a cortical marker of persistent cognitive dysfunction after traumatic brain injury (TBI). Because the RSC is easily accessible at the brain's surface using in vivo techniques, we argue that studying RSC network activity post-TBI can shed light into the mechanisms of less-accessible brain regions, such as the hippocampus. There is a fundamental gap in the TBI field about the microscale alterations occurring post-trauma, and by studying the RSC's neuronal activity at the cellular level we will be able to design better therapeutic tools. Understanding how neuronal activity and interactions produce normal and abnormal activity in the injured brain is crucial to understanding cognitive dysfunction. By using this approach, we expect to gain valuable insights to better understand brain disorders like TBI.

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Chronic generalized pain disrupts whole brain functional connectivity in mice

Cited by 10 publications

References 48 publications

Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics

Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics

Image Features of Resting‐State Functional Magnetic Resonance Imaging in Evaluating Poor Emotion and Sleep Quality in Patients with Chronic Pain under Artificial Intelligence Algorithm

Peering into the Brain through the Retrosplenial Cortex to Assess Cognitive Function of the Injured Brain

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