Dataset Information

Dysregulation of Pain- and Emotion-Related Networks in Trigeminal Neuralgia.

ABSTRACT: Classical trigeminal neuralgia (TN) is a severe neuropathic facial pain disorder associated with increased risks of anxiety and depression. Converging evidence suggests that chronic pain pathophysiology involves dysfunctional pain-related and emotion-related networks. However, whether these systems are also among the culprit networks for TN remains unclear. Here, we aimed to assess TN-related anatomical and functional brain anomalies in pain-related and emotion-related networks. We investigated differences in gray matter (GM) volume and the related resting-state functional connectivity (rsFC) between 29 classical TN patients and 34 matched healthy controls. Relationships between brain measurement alterations, clinical pain and emotional states were identified. A longitudinal observation was further conducted to determine whether alterations in the brain could renormalize following pain relief. Reduced GM volumes in the bilateral amygdala, periaqueductal gray (PAG) and right insula were found in TN patients compared with healthy control subjects. Whole-brain rsFC analyses with the four above-mentioned anatomical regions as seeds identified three significantly altered functional circuits, including amygdala-DLPFC, amygdala-mPFC and amygdala-thalamus/putamen circuitry. The amygdala-DLPFC and amygdala-mPFC circuits were associated with clinical pain duration and emotional state ratings, respectively. Further longitudinal analysis found that rsFC strength abnormalities in two fronto-limbic circuits (left amygdala/left DLPFC and right amygdala/right PFC) were resolved after pain relief. Together, structural and functional deficits in pain-related and emotion-related networks were associated with TN patients, as demonstrated by our multimodal results. Pain relief had protective effects on brain functional connectivity within fronto-limbic circuits. Our study provides novel insights into the pathophysiology of TN, which may ultimately facilitate advances in TN intervention.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5890150 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Dysregulation of Pain- and Emotion-Related Networks in Trigeminal Neuralgia.

Zhang Yanyang Y Mao Zhiqi Z Pan Longsheng L Ling Zhipei Z Liu Xinyun X Zhang Jun J Yu Xinguang X

Frontiers in human neuroscience 20180320

Classical trigeminal neuralgia (TN) is a severe neuropathic facial pain disorder associated with increased risks of anxiety and depression. Converging evidence suggests that chronic pain pathophysiology involves dysfunctional pain-related and emotion-related networks. However, whether these systems are also among the culprit networks for TN remains unclear. Here, we aimed to assess TN-related anatomical and functional brain anomalies in pain-related and emotion-related networks. We investigated ...[more]

PMID: 29662445

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Project description:BackgroundTrigeminal neuralgia, a severe chronic neuropathic pain disorder, is widely believed to be amenable to surgical treatments. Nearly 20% of patients, however, do not have adequate pain relief after surgery. Objective tools for personalized pre-treatment prognostication of pain relief following surgical interventions can minimize unnecessary surgeries and thus are of substantial benefit for patients and clinicians.PurposeTo determine if pre-treatment regional brain morphology-based machine learning models can prognosticate 1 year response to Gamma Knife radiosurgery for trigeminal neuralgia.MethodsWe used a data-driven approach that combined retrospective structural neuroimaging data and support vector machine-based machine learning to produce robust multivariate prediction models of pain relief following Gamma Knife radiosurgery for trigeminal neuralgia. Surgical response was defined as ≥ 75% pain relief 1 year post-treatment. We created two prediction models using pre-treatment regional brain gray matter morphology (cortical thickness or surface area) to distinguish responders from non-responders to radiosurgery. Feature selection was performed through sequential backwards selection algorithm. Model out-of-sample generalizability was estimated via stratified 10-fold cross-validation procedure and permutation testing.ResultsIn 51 trigeminal neuralgia patients (35 responders, 16 non-responders), machine learning models based on pre-treatment regional brain gray matter morphology (14 regional surface areas or 13 regional cortical thicknesses) provided robust a priori prediction of surgical response. Cross-validation revealed the regional surface area model was 96.7% accurate, 100.0% sensitive, and 89.1% specific while the regional cortical thickness model was 90.5% accurate, 93.5% sensitive, and 83.7% specific. Permutation testing revealed that both models performed beyond pure chance (p < 0.001). The best predictor for regional surface area model and regional cortical thickness model was contralateral superior frontal gyrus and contralateral isthmus cingulate gyrus, respectively.ConclusionsOur findings support the use of machine learning techniques in subsequent investigations of chronic neuropathic pain. Furthermore, our multivariate framework provides foundation for future development of generalizable, artificial intelligence-driven tools for chronic neuropathic pain treatments.

Project description:ObjectiveTo compare complete neuroendoscopic and microscopic microvascular decompression (MVD) in primary trigeminal neuralgia (PTN) and their impacts on the microstructure of the trigeminal nerve.MethodsEighty-seven PTN patients admitted in our hospital from July 2017 to December 2019 were selected for this prospective study and divided into the endoscopic group (n=45) (complete neuroendoscopic MVD) and the microscope group (n=42) (microscopic MVD) according to the treatment method each patient underwent. All the patients underwent MRI scanning, and the fractional anisotropy (FA) scores and the apparent diffusion coefficient (ADC) values of the neurovascular compression (NVC) sites were measured. The operation times, the treatment efficacy, the microstructural changes in the trigeminal nerve, the complications, and the recurrence and mortality rates at one year after the operations were compared.ResultsThe endoscopic group observed a superior therapeutic effect compared with the microscope group one year after the surgeries (P=0.046). After the surgeries, the endoscopic group observed a greater increase in their FA values and a larger decline in their ADC values than the microscope group did (P=0.014, 0.015, 0.011, 0.002). The complication rate in the endoscopic group was 11.11%, and the complication rate in the microscopic group was 30.95% (P=0.022). One year after the surgeries, we found a lower recurrence rate in the endoscopic group (P=0.001). The perforator vessels from the offending vessel to the outlet area of the durmedulla, the distances between the front edge of the bone window and the inner surfaces of the petrous part of the temporal bone ≥ the distance between the duration ≥ the duration of conventional MVD were independent risk factors for complications after MVD in the hemifacial spasm patients (P=0.001, 0.037, 0.023, 0.005).ConclusionComplete neuroendoscopic MV yields better long-term treatment outcomes than microscopic MVD, and it is more effective at improving the microstructure of the trigeminal nerve and has fewer postoperative complications.

Dataset Information

Dysregulation of Pain- and Emotion-Related Networks in Trigeminal Neuralgia.

Publications

Dysregulation of Pain- and Emotion-Related Networks in Trigeminal Neuralgia.

Similar Datasets

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