A neuroimaging biomarker for sustained experimental and clinical pain.
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ABSTRACT:
SUBMITTER: Lee JJ
PROVIDER: S-EPMC8447264 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:With the development of real-time and visualized neuroimaging techniques, the studies on the central mechanism of acupuncture analgesia gain increasing attention. The experimental pain models have been widely used in acupuncture-analgesia neuroimaging studies with quantitative and controlled advantages. This review aimed to analyze the study design and main findings of acupuncture neuroimaging studies to provide reference for future study. The original studies were collected and screened in English databases (PubMed, EMBASE, and Cochrane Library) and Chinese databases (Chinese Nation Knowledge Infrastructure, Chinese Biomedical Literature Database, the Chongqing VIP Database, and Wanfang Database). As a result, a total of 27 articles were included. Heat stimulation and electroacupuncture were the mostly used pain modeling method and acupuncture modality, respectively. The neuroimaging scanning process can be divided into two models and five subtypes. The anterior cingulate cortex and insula were the most commonly reported brain regions involved in acupuncture analgesia with experimental pain models.
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Project description:Pain is an unpleasant sensory and emotional experience. Understanding the neural mechanisms of acute and chronic pain and the brain changes affecting pain factors is important for finding pain treatment methods. The emergence and progress of non-invasive neuroimaging technology can help us better understand pain at the neural level. Recent developments in identifying brain-based biomarkers of pain through advances in advanced imaging can provide some foundations for predicting and detecting pain. For example, a neurologic pain signature (involving brain regions that receive nociceptive afferents) and a stimulus intensity-independent pain signature (involving brain regions that do not show increased activity in proportion to noxious stimulus intensity) were developed based on multivariate modeling to identify processes related to the pain experience. However, an accurate and comprehensive review of common neuroimaging techniques for evaluating pain is lacking. This paper reviews the mechanism, clinical application, reliability, strengths, and limitations of common neuroimaging techniques for assessing pain to promote our further understanding of pain.
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Project description:Neurobiological pain models propose that chronic pain is accompanied by neurofunctional changes that mediate pain processing dysfunctions. In contrast, meta-analyses of neuroimaging studies in chronic pain conditions have not revealed convergent evidence for robust alterations during experimental pain induction. Against this background, the present neuroimaging meta-analysis combined three different meta-analytic approaches with stringent study selection criteria for case-control functional magnetic resonance imaging experiments during acute pain processing with a focus on chronic pain disorders. Convergent neurofunctional dysregulations in chronic pain patients were observed in the left anterior insula cortex. Seed-based resting-state functional connectivity based on a large publicly available dataset combined with a meta-analytic task-based approach identified the anterior insular region as a key node of an extended bilateral insula-fronto-cingular network, resembling the salience network. Moreover, the meta-analytic decoding showed that this region presents a high probability to be specifically activated during pain-related processes, although we cannot exclude an involvement in autonomic processes. Together, the present findings indicate that dysregulated left anterior insular activity represents a robust neurofunctional maladaptation and potential treatment target in chronic pain disorders.
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Project description:Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influenced by multidimensional contextual variables. Although objective biomarkers for pain could substantially aid pain diagnosis and development of novel therapies, reliable markers for clinical pain have been elusive. In this study, individualized physical maneuvers were used to exacerbate clinical pain in patients with chronic low back pain (N = 53), thereby experimentally producing lower and higher pain states. Multivariate machine-learning models were then built from brain imaging (resting-state blood-oxygenation-level-dependent and arterial spin labeling functional imaging) and autonomic activity (heart rate variability) features to predict within-patient clinical pain intensity states (ie, lower vs higher pain) and were then applied to predict between-patient clinical pain ratings with independent training and testing data sets. Within-patient classification between lower and higher clinical pain intensity states showed best performance (accuracy = 92.45%, area under the curve = 0.97) when all 3 multimodal parameters were combined. Between-patient prediction of clinical pain intensity using independent training and testing data sets also demonstrated significant prediction across pain ratings using the combined model (Pearson's r = 0.63). Classification of increased pain was weighted by elevated cerebral blood flow in the thalamus, and prefrontal and posterior cingulate cortices, and increased primary somatosensory connectivity to frontoinsular cortex. Our machine-learning approach introduces a model with putative biomarkers for clinical pain and multiple clinical applications alongside self-report, from pain assessment in noncommunicative patients to identification of objective pain endophenotypes that can be used in future longitudinal research aimed at discovery of new approaches to combat chronic pain.
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Project description:Uncoupling the protein-protein interaction between collapsin response mediator protein 2 (CRMP2) and N-type voltage-gated calcium channel (CaV2.2) with an allosteric CRMP2-derived peptide (CBD3) is antinociceptive in rodent models of inflammatory and neuropathic pain. We investigated the efficacy, duration of action, abuse potential, and neurobehavioral toxicity of an improved mutant CRMP2 peptide. A homopolyarginine (R9)-conjugated CBD3-A6K (R9-CBD3-A6K) peptide inhibited the CaV2.2-CRMP2 interaction in a concentration-dependent fashion and diminished surface expression of CaV2.2 and depolarization-evoked Ca influx in rat dorsal root ganglia neurons. In vitro studies demonstrated suppression of excitability of small-to-medium diameter dorsal root ganglion and inhibition of subtypes of voltage-gated Ca channels. Sprague-Dawley rats with tibial nerve injury had profound and long-lasting tactile allodynia and ongoing pain. Immediate administration of R9-CBD3-A6K produced enhanced dopamine release from the nucleus accumbens shell selectively in injured animals, consistent with relief of ongoing pain. R9-CBD3-A6K, when administered repeatedly into the central nervous system ventricles of naive rats, did not result in a positive conditioned place preference demonstrating a lack of abusive liability. Continuous subcutaneous infusion of R9-CBD3-A6K over a 24- to 72-hour period reversed tactile allodynia and ongoing pain, demonstrating a lack of tolerance over this time course. Importantly, continuous infusion of R9-CBD3-A6K did not affect motor activity, anxiety, depression, or memory and learning. Collectively, these results validate the potential therapeutic significance of targeting the CaV-CRMP2 axis for treatment of neuropathic pain.
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Project description:NCT03343197: Clinical Biomarker Data
| PRJNA909914 | ENA
Project description:NCT03343197: Clinical Biomarker Data
| PRJNA909913 | ENA
Project description:BackgroundEpidemiologic studies have shown that Alzheimer's disease (AD) and related dementias (ADRD) are seen more frequently with asthma, especially with greater asthma severity or exacerbation frequency.ObjectiveTo examine the changes in brain structure that may underlie this phenomenon, we examined diffusion-weighted magnetic resonance imaging (dMRI) and blood-based biomarkers of AD (phosphorylated tau 181, p-Tau181), neurodegeneration (neurofilament light chain, NfL), and glial activation (glial fibrillary acidic protein, GFAP).MethodsdMRI data were obtained in 111 individuals with asthma, ranging in disease severity from mild to severe, and 135 healthy controls. Regression analyses were used to test the relationships between asthma severity and neuroimaging measures, as well as AD pathology, neurodegeneration, and glial activation, indexed by plasma p-Tau181, NfL, and GFAP, respectively. Additional relationships were tested with cognitive function.ResultsAsthma participants had widespread and large-magnitude differences in several dMRI metrics, which were indicative of neuroinflammation and neurodegeneration, and which were robustly associated with GFAP and, to a lesser extent, NfL. The AD biomarker p-Tau181 was only minimally associated with neuroimaging outcomes. Further, asthma severity was associated with deleterious changes in neuroimaging outcomes, which in turn were associated with slower processing speed, a test of cognitive performance.ConclusionsAsthma, particularly when severe, is associated with characteristics of neuroinflammation and neurodegeneration, and may be a potential risk factor for neural injury and cognitive dysfunction. There is a need to determine how asthma may affect brain health and whether treatment directed toward characteristics of asthma associated with these risks can mitigate these effects.
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Project description:Pain in knee osteoarthritis (OA) has historically been attributed to peripheral pathophysiology; however, the poor correspondence between objective measures of disease severity and clinical symptoms suggests that non-local factors, such as altered central processing of painful stimuli, also contribute to clinical pain in knee OA. Consistent with this notion, recent evidence demonstrates that patients with knee OA exhibit increased sensitivity to painful stimuli at body sites unaffected by clinical pain.In order to further investigate the contribution of altered pain processing to knee OA pain, the current study tested the hypothesis that symptomatic knee OA is associated with enhanced sensitivity to experimental pain stimuli at the knee and at remote body sites unaffected by clinical pain. We further anticipated that pain sensitivity would differ as a function of the OA symptom severity. Older adults with and without symptomatic knee OA completed a series of experimental pain assessments. A median split of the Western Ontario and McMaster Universities Index of Osteoarthritis (WOMAC) was used to stratify participants into low vs high OA symptom severity.Compared to controls and the low symptom group, individuals in the high symptom group were more sensitive to suprathreshold heat stimuli, blunt pressure, punctuate mechanical, and cold stimuli. Individuals in the low symptomatic OA group subgroup exhibited experimental pain responses similar to the pain-free group on most measures. No group differences in endogenous pain inhibition emerged.These findings suggest that altered central processing of pain is particularly characteristic of individuals with moderate to severe symptomatic knee OA.
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Project description:Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain networks are reconfigured over time while experiencing pain. Here, we investigated the time-varying changes in the functional brain networks during 20 min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from other areas of the somatosensory cortex and integrated with subcortical and frontoparietal regions, constituting an extended brain network of sustained pain. As pain decreased over time, the subcortical and frontoparietal regions were separated from this brain network and connected to multiple cerebellar regions. Machine-learning models based on these network features showed significant predictions of changes in pain experience across two independent datasets (n = 48 and 74). This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, advancing our mechanistic understanding of sustained pain.
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