Dataset Information

Biomarkers in Temporomandibular Disorder and Trigeminal Neuralgia: A Conceptual Framework for Understanding Chronic Pain.

ABSTRACT: In this review, we will explore the use of biomarkers in chronic pain, using the examples of two prototypical facial pain conditions: trigeminal neuralgia and temporomandibular disorder. We will discuss the main categories of biomarkers and identify various genetic/genomic, molecular, neuroradiological, and psychophysical biomarkers in both facial pain conditions, using them to compare and contrast features of neuropathic, non-neuropathic, and mixed pain. By using two distinct model facial pain conditions to explore pain biomarkers, we aim to familiarize readers with different types of biomarkers currently being studied in chronic pain, and explore how these biomarkers may be used to develop new precision medicine approaches to pain diagnosis, prognosis, and management.

SUBMITTER: Doshi TL

PROVIDER: S-EPMC7486013 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Biomarkers in Temporomandibular Disorder and Trigeminal Neuralgia: A Conceptual Framework for Understanding Chronic Pain.

Doshi Tina L TL Nixdorf Donald R DR Campbell Claudia M CM Raja Srinivasa N SN

Canadian journal of pain = Revue canadienne de la douleur 20200123 1

In this review, we will explore the use of biomarkers in chronic pain, using the examples of two prototypical facial pain conditions: trigeminal neuralgia and temporomandibular disorder. We will discuss the main categories of biomarkers and identify various genetic/genomic, molecular, neuroradiological, and psychophysical biomarkers in both facial pain conditions, using them to compare and contrast features of neuropathic, non-neuropathic, and mixed pain. By using two distinct model facial pain ...[more]

PMID: 32923920

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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: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.

Dataset Information

Biomarkers in Temporomandibular Disorder and Trigeminal Neuralgia: A Conceptual Framework for Understanding Chronic Pain.

Publications

Biomarkers in Temporomandibular Disorder and Trigeminal Neuralgia: A Conceptual Framework for Understanding Chronic Pain.

Similar Datasets

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