Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia

Advanced neuroimaging of the trigeminal nerve and the whole brain in trigeminal neuralgia: a systematic review

ABSTRACT

For trigeminal neuralgia (TN), a major role of imaging is to identify the causes, but recent studies demonstrated structural and microstructural changes in the affected nerve. Moreover, an increasing number of studies have reported central nervous system involvement in TN. In this systematic review, recent quantitative magnetic resonance imaging (MRI) studies of the trigeminal nerve and the brain in patients with TN were compiled, organized, and discussed, particularly emphasizing the possible background mechanisms and the interpretation of the results. A systematic search of quantitative MRI studies of the trigeminal nerve and the brain in patients with TN was conducted using PubMed. We included the studies of the primary TN published during 2013 to 2023, conducted for the assessment of the structural and microstructural analysis of the trigeminal nerve, and the structural, diffusion, and functional MRI analysis of the brain. Quantitative MRI studies of the affected trigeminal nerves and the trigeminal pathway demonstrated structural/microstructural alterations and treatment-related changes, which differentiated responders from nonresponders. Quantitative analysis of the brain revealed changes in the brain areas associated with pain processing/modulation and emotional networks. Studies of the affected nerve demonstrated evidence of demyelination and axonal damage, compatible with pathological findings, and have shown its potential value as a tool to assess treatment outcomes. Quantitative MRI has also revealed the possibility of dynamic microstructural, structural, and functional neuronal plasticity of the brain. Further studies are needed to understand these complex mechanisms of neuronal plasticity and to achieve a consensus on the clinical use of quantitative MRI in TN.

Sex-Specific White Matter Abnormalities Across the Dynamic Pain Connectome in Neuropathic Pain: A Fixel-Based Analysis Study

A fundamental issue in neuroscience is a lack of understanding regarding the relationship between brain function and the white matter architecture that supports it. Individuals with chronic neuropathic pain (NP) exhibit functional abnormalities throughout brain networks collectively termed the "dynamic pain connectome" (DPC), including the default mode network (DMN), salience network, and ascending nociceptive and descending pain modulation systems. These functional abnormalities are often observed in a sex-dependent fashion. However, the enigmatic white matter structural features underpinning these functional networks and the relationship between structure and function/dysfunction in NP remain poorly understood. Here we used fixel-based analysis of diffusion weighted imaging data in 80 individuals (40 with NP [21 female, 19 male] and 40 sex- and age-matched healthy controls [HCs]) to evaluate white matter microstructure (fiber density [FD]), macrostructure (fiber bundle cross section) and combined microstructure and macrostructure (fiber density and cross section) within anatomical connections that support the DPC. We additionally examined whether there are sex-specific abnormalities in NP white matter structure. We performed fixel-wise and connection-specific mean analyses and found three main ways in which individuals with NP differed from HCs: (1) people with NP exhibited abnormally low FD and FDC within the corona radiata consistent with the ascending nociceptive pathway between the sensory thalamus and primary somatosensory cortex (S1). Furthermore, the entire sensory thalamus-S1 pathway exhibited abnormally low FD and FDC in people with NP, and this effect was driven by the females with NP; (2) females, but not males, with NP had abnormally low FD within the cingulum consistent with the right medial prefrontal cortex-posterior cingulate cortex DMN pathway; and (3) individuals with NP had higher connection-specific mean FDC than HCs in the anterior insula-temporoparietal junction and sensory thalamus-posterior insula pathways. However, sex-specific analyses did not corroborate these connection-specific findings in either females or males with NP. Our findings suggest that females with NP exhibit microstructural and macrostructural white matter abnormalities within the DPC networks including the ascending nociceptive system and DMN. We propose that aberrant white matter structure contributes to or is driven by functional abnormalities associated with NP. Our sex-specific findings highlight the utility and importance of using sex-disaggregated analyses to identify white matter abnormalities in clinical conditions such as chronic pain.

Regional and interregional functional and structural brain abnormalities in neuropathic pain

Neuropathic pain is a severe form of chronic pain due to a lesion or disease of the somatosensory nervous system. Here we provide an overview of the neuroimaging approaches that can be used to assess brain abnormalities in a chronic pain condition, with particular focus on people with neuropathic pain and then summarize the findings of studies that applied these methodologies to study neuropathic pain. First, we review the most commonly used approaches to examine grey and white matter abnormalities using magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) and then review functional neuroimaging techniques to measure regional activity and inter-regional communication using functional MRI, electroencephalography (EEG) and magnetoencephalography (MEG). In neuropathic pain the most prominent structural abnormalities have been found to be in the primary somatosensory cortex, insula, anterior cingulate cortex and thalamus, with differences in volume directionality linked to neuropathic pain symptomology. Functional connectivity findings related to treatment outcome point to a potential clinical utility. Some prominent abnormalities in neuropathic pain identified with EEG and MEG throughout the dynamic pain connectome are slowing of alpha activity and higher regional oscillatory activity in the theta and alpha band, lower low beta and higher high beta band power. Finally, connectivity and coupling findings placed into context how regional abnormalities impact the networks and pathways of the dynamic pain connectome. Overall, functional and structural neuroimaging have the potential to identify predictive biomarkers that can be used to guide development of personalized pain management of neuropathic pain.

Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics

BACKGROUND

This study aims to utilize the deep learning method of VB-Net to locate and segment the trigeminal nerve, and employ radiomics methods to distinguish between CTN patients and healthy individuals.

METHODS

A total of 165 CTN patients and 175 healthy controls, matched for gender and age, were recruited. All subjects underwent magnetic resonance scans. VB-Net was used to locate and segment the bilateral trigeminal nerve of all subjects, followed by the application of radiomics methods for feature extraction, dimensionality reduction, feature selection, model construction, and model evaluation.

RESULTS

On the test set for trigeminal nerve segmentation, our segmentation parameters are as follows: the mean Dice Similarity Coefficient (mDCS) is 0.74, the Average Symmetric Surface Distance (ASSD) is 0.64 mm, and the Hausdorff Distance (HD) is 3.34 mm, which are within the acceptable range. Analysis of CTN patients and healthy controls identified 12 features with larger weights, and there was a statistically significant difference in Rad_score between the two groups (p < 0.05). The Area Under the Curve (AUC) values for the three models (Gradient Boosting Decision Tree, Gaussian Process, and Random Forest) are 0.90, 0.87, and 0.86, respectively. After testing with DeLong and McNemar methods, these three models all exhibit good performance in distinguishing CTN from normal individuals.

CONCLUSIONS

Radiomics can aid in the clinical diagnosis of CTN, and it is a more objective approach. It serves as a reliable neurobiological indicator for the clinical diagnosis of CTN and the assessment of changes in the trigeminal nerve in patients with CTN.

Pretreatment Brain White Matter Integrity Associated With Neuropathic Pain Relief and Changes in Temporal Summation of Pain Following Ketamine

Neuropathic pain (NP) is a prevalent condition often associated with heightened pain responsiveness suggestive of central sensitization. Neuroimaging biomarkers of treatment outcomes may help develop personalized treatment strategies, but white matter (WM) properties have been underexplored for this purpose. Here we assessed whether WM pathways of the default mode network (DMN: medial prefrontal cortex [mPFC], posterior cingulate cortex, and precuneus) and descending pain modulation system (periaqueductal gray [PAG]) are associated with ketamine analgesia and attenuated temporal summation of pain (TSP, reflecting central sensitization) in NP. We used a fixel-based analysis of diffusion-weighted imaging data to evaluate WM microstructure (fiber density [FD]) and macrostructure (fiber bundle cross-section) within the DMN and mPFC-PAG pathways in 70 individuals who underwent magnetic resonance imaging and TSP testing; 35 with NP who underwent ketamine treatment and 35 age- and sex-matched pain-free individuals. Individuals with NP were assessed before and 1 month after treatment; those with ≥30% pain relief were considered responders (n = 18), or otherwise as nonresponders (n = 17). We found that WM structure within the DMN and mPFC-PAG pathways did not differentiate responders from nonresponders. However, pretreatment FD in the anterior limb of the internal capsule correlated with pain relief (r=.48). Moreover, pretreatment FD in the DMN (left mPFC-precuneus/posterior cingulate cortex; r=.52) and mPFC-PAG (r=.42) negatively correlated with changes in TSP. This suggests that WM microstructure in the DMN and mPFC-PAG pathway is associated with the degree to which ketamine reduces central sensitization. Thus, fixel metrics of WM structure may hold promise to predict ketamine NP treatment outcomes. PERSPECTIVE: We used advanced fixel-based analyses of MRI diffusion-weighted imaging data to identify pretreatment WM microstructure associated with ketamine outcomes, including analgesia and markers of attenuated central sensitization. Exploring associations between brain structure and treatment outcomes could contribute to a personalized approach to treatment for individuals with NP.

Deciphering the functional role of insular cortex stratification in trigeminal neuropathic pain

Trigeminal neuropathic pain (TNP) is a major concern in both dentistry and medicine. The progression from normal to chronic TNP through activation of the insular cortex (IC) is thought to involve several neuroplastic changes in multiple brain regions, resulting in distorted pain perception and associated comorbidities. While the functional changes in the insula are recognized contributors to TNP, the intricate mechanisms underlying the involvement of the insula in TNP processing remain subjects of ongoing investigation. Here, we have overviewed the most recent advancements regarding the functional role of IC in regulating TNP alongside insights into the IC's connectivity with other brain regions implicated in trigeminal pain pathways. In addition, the review examines diverse modulation strategies that target the different parts of the IC, thereby suggesting novel diagnostic and therapeutic management of chronic TNP in the future.

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research

OBJECTIVE

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management.

METHODS

A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used.

RESULTS

After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP.

CONCLUSION

The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures.

SIGNIFICANCE

This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Analyzing the risk factors of unilateral trigeminal neuralgia under neurovascular compression

Background

This study aimed to explore the risk factors and potential causes of unilateral classical or idiopathic trigeminal neuralgia (C-ITN) by comparing patients and healthy controls (HCs) with neurovascular compression (NVC) using machine learning (ML).

Methods

A total of 84 C-ITN patients and 78 age- and sex-matched HCs were enrolled. We assessed the trigeminal pons angle and identified the compressing vessels and their location and severity. Machine learning was employed to analyze the cisternal segment of the trigeminal nerve (CN V).

Results

Among the C-ITN patients, 53 had NVC on the unaffected side, while 25 HCs exhibited bilateral NVC, and 24 HCs showed unilateral NVC. By comparing the cisternal segment of CN V between C-ITN patients on the affected side and HCs with NVC, we identified the side of NVC, the compressing vessel, and certain texture features as risk factors for C-ITN. Additionally, four texture features differed in the structure of the cisternal segment of CN V between C-ITN patients on the unaffected side and HCs with NVC.

Conclusion

Our findings suggest that the side of NVC, the compressing vessel, and the microstructure of the cisternal segment of CN V are associated with the risk of C-ITN. Furthermore, microstructural changes observed in the cisternal segment of CN V on the unaffected side of C-ITN patients with NVC indicate possible indirect effects on the CN V to some extent.

Altered trends of local brain function in classical trigeminal neuralgia patients after a single trigger pain

OBJECTIVE

To investigate the altered trends of regional homogeneity (ReHo) based on time and frequency, and clarify the time-frequency characteristics of ReHo in 48 classical trigeminal neuralgia (CTN) patients after a single pain stimulate.

METHODS

All patients underwent three times resting-state functional MRI (before stimulation (baseline), after stimulation within 5 s (triggering-5 s), and in the 30th min of stimulation (triggering-30 min)). The spontaneous brain activity was investigated by static ReHo (sReHo) in five different frequency bands and dynamic ReHo (dReHo) methods.

RESULTS

In the five frequency bands, the number of brain regions which the sReHo value changed in classical frequency band were most, followed by slow 4 frequency band. The left superior occipital gyrus was only found in slow 2 frequency band and the left superior parietal gyrus was only found in slow 3 frequency band. The dReHo values were changed in midbrain, left thalamus, right putamen, and anterior cingulate cortex, which were all different from the brain regions that the sReHo value altered. There were four altered trends of the sReHo and dReHo, which dominated by decreased at triggering-5 s and increased at triggering-30 min.

CONCLUSIONS

The duration of brain function changed was more than 30 min after a single pain stimulate, although the pain of CTN was transient. The localized functional homogeneity has time-frequency characteristic in CTN patients after a single pain stimulate, and the changed brain regions of the sReHo in five frequency bands and dReHo complemented to each other. Which provided a certain theoretical basis for exploring the pathophysiology of CTN.

The role of artificial intelligence in the management of trigeminal neuralgia

Trigeminal neuralgia (TN) is the most frequent facial pain. It is difficult to treat pharmacologically and a significant amount of patients can become drug-resistant requiring surgical intervention. From an etiologically point of view TN can be distinguished in a classic form, usually due to a neurovascular conflict, a secondary form (for example related to multiple sclerosis or a cerebello-pontine angle tumor) and an idiopathic form in which no anatomical cause is identifiable. Despite numerous efforts to treat TN, many patients experience recurrence after multiple operations. This fact reflects our incomplete understanding of TN pathogenesis. Artificial intelligence (AI) uses computer technology to develop systems for extension of human intelligence. In the last few years, it has been a widespread of AI in different areas of medicine to implement diagnostic accuracy, treatment selection and even drug production. The aim of this mini-review is to provide an up to date of the state-of-art of AI applications in TN diagnosis and management.

Brain imaging signatures of neuropathic facial pain derived by artificial intelligence

Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them from healthy controls. We conducted a retrospective analysis of diffusion tensor and T1-weighted imaging data using random forest and logistic regression AI models on 371 adults with trigeminal pain (265 classical trigeminal neuralgia (CTN), 106 trigeminal neuropathic pain (TNP)) and 108 healthy controls (HC). These models distinguished CTN from HC with up to 95% accuracy, and TNP from HC with up to 91% accuracy. Both classifiers identified gray and white matter-based predictive metrics (gray matter thickness, surface area, and volume; white matter diffusivity metrics) that significantly differed across groups. Classification of TNP and CTN did not show significant accuracy (51%) but highlighted two structures that differed between pain groups-the insula and orbitofrontal cortex. Our work demonstrates that AI models with brain imaging data alone can differentiate neuropathic facial pain subtypes from healthy data and identify regional structural indicates of pain.

Functional MRI-Guided Motor Cortex and Deep Brain Stimulation for Intractable Facial Pain: A Novel, Personalized Approach in 1 Patient

BACKGROUND

Facial neuropathic pain syndromes such as trigeminal neuralgia are debilitating disorders commonly managed by medications, vascular decompression, and/or ablative procedures. In trigeminal neuralgia cases unresponsive to these interventions, trigeminal deafferentation pain syndrome (TDPS) can emerge and remain refractory to any further attempts at these conventional therapies. Deep brain stimulation (DBS) and motor cortex stimulation are 2 neuromodulatory treatments that have demonstrated efficacy in small case series of TDPS yet remain largely underutilized. In addition, functional MRI (fMRI) is a tool that can help localize central processing of evoked stimuli such as mechanically triggered facial pain. In this study, we present a case report and operative technique in a patient with TDPS who underwent fMRI to guide the operative management and placement of dual targets in the sensory thalamus and motor cortex.

OBJECTIVE

To evaluate the safety, efficacy, and outcome of a novel surgical approach for TDPS in a single patient.

METHODS

The fMRI and operative technique of unilateral DBS targeting the ventroposteromedial nucleus of the thalamus and facial motor cortex stimulator placement through a single burr hole is illustrated as well as the patient's clinical outcome.

RESULTS

In less than 1 year, the patient had near complete resolution of his facial pain with no postoperative complications.

CONCLUSION

We present the first published case of successful treatment of TDPS using simultaneous DBS of the ventroposteromedial and motor cortex stimulation. fMRI can be used as an effective imaging modality to guide neuromodulation in this complex disorder.

Altered Structural and Functional Abnormalities of Hippocampus in Classical Trigeminal Neuralgia: A Combination of DTI and fMRI Study

Purpose

Diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) were applied to speculate the altered structural and functional abnormalities within the hippocampus in classical trigeminal neuralgia (CTN) patients by detecting the alteration of apparent diffusion coefficient (ADC), fractional anisotropy (FA), and regional homogeneity (ReHo). Patients and Methods. Multimodal MRI dataset (DTI and fMRI) and clinical indices (pain and neuropsychological scores) were collected in 27 CTN patients and 27 age- and gender-matched healthy controls (HC). Two independent-sample t-tests were performed to compare the ADC, FA, and ReHo values in hippocampus areas between CTN patients and HC. Correlation analyses were applied between all the DTI and fMRI parameters within the hippocampus and the VAS (visual analog scale), MoCA (Montreal cognitive assessment), and CDR (clinical dementia rating) scores.

Results

CTN patients showed a significantly increased FA values in the right hippocampus (t = 2.387, P = 0.021) and increased ReHo values in the right hippocampus head (voxel P < 0.001, cluster P < 0.05, FDR correction) compared with HC. A positively significant correlation was observed between the ReHo values and MOCA scores in the right hippocampus head; FA values were also positively correlated with MOCA scores in the right hippocampus.

Conclusion

CTN patients demonstrated an abnormality of structures and functions in the hippocampus, which may help to provide novel insights into the neuropathologic change related to the pain-related dysfunction of CTN.

The missing mechanistic link: Improving behavioral treatment efficacy for pediatric chronic pain

Pediatric chronic pain is a significant global issue, with biopsychosocial factors contributing to the complexity of the condition. Studies have explored behavioral treatments for pediatric chronic pain, but these treatments have mixed efficacy for improving functional and psychological outcomes. Furthermore, the literature lacks an understanding of the biobehavioral mechanisms contributing to pediatric chronic pain treatment response. In this mini review, we focus on how neuroimaging has been used to identify biobehavioral mechanisms of different conditions and how this modality can be used in mechanistic clinical trials to identify markers of treatment response for pediatric chronic pain. We propose that mechanistic clinical trials, utilizing neuroimaging, are warranted to investigate how to optimize the efficacy of behavioral treatments for pediatric chronic pain patients across pain types and ages.

Structural and Functional Brain Changes in Patients With Classic Trigeminal Neuralgia: A Combination of Voxel-Based Morphometry and Resting-State Functional MRI Study

Objectives

Brain structural and functional abnormalities have been separately reported in patients with classic trigeminal neuralgia (CTN). However, whether and how the functional deficits are related to the structural alterations remains unclear. This study aims to investigate the anatomical and functional deficits in patients with CTN and explore their association.

Methods

A total of 34 patients with CTN and 29 healthy controls (HCs) with age- and gender-matched were recruited. All subjects underwent structural and resting-state functional magnetic resonance imaging (fMRI) scanning and neuropsychological assessments. Voxel-based morphometry (VBM) was applied to characterize the alterations of gray matter volume (GMV). The amplitude of low-frequency fluctuation (ALFF) method was used to evaluate regional intrinsic spontaneous neural activity. Further correlation analyses were performed between the structural and functional changes and neuropsychological assessments.

Results

Compared to the HCs, significantly reduced GMV was revealed in the right hippocampus, right fusiform gyrus (FFG), and temporal-parietal regions (the left superior/middle temporal gyrus, left operculo-insular gyrus, left inferior parietal lobule, and right inferior temporal gyrus) in patients with CTN. Increased functional activity measured by zALFF was observed mainly in the limbic system (the bilateral hippocampus and bilateral parahippocampal gyrus), bilateral FFG, basal ganglia system (the bilateral putamen, bilateral caudate, and right pallidum), left thalamus, left cerebellum, midbrain, and pons. Moreover, the right hippocampus and FFG were the overlapped regions with both functional and anatomical deficits. Furthermore, GMV in the right hippocampus was negatively correlated with pain intensity, anxiety, and depression. GMV in the right FFG was negatively correlated with illness duration. The zALFF value in the right FFG was positively correlated with anxiety.

Conclusion

Our results revealed concurrent structural and functional changes in patients with CTN, indicating that the CTN is a brain disorder with structural and functional abnormalities. Moreover, the overlapping structural and functional changes in the right hippocampus and FFG suggested that anatomical and functional changes might alter dependently in patients with CTN. These findings highlight the vital role of hippocampus and FFG in the pathophysiology of CTN.

White Matter Diffusion Properties in Chronic Temporomandibular Disorders: An Exploratory Analysis

Objective

To determine differences in diffusion metrics in key white matter (WM) tracts between women with chronic temporomandibular disorders (TMDs) and age- and sex-matched healthy controls.

Design

Cross sectional study compared diffusion metrics between groups and explored their associations with clinical variables in subjects with TMDs.

Methods

In a total of 33 subjects with TMDs and 33 healthy controls, we performed tractography to obtain diffusion metrics (fractional anisotropy [FA], mean diffusivity [MD], radial diffusivity [RD], and axial diffusivity [AD]) from the cingulum near the cingulate gyrus (CGC), the cingulum near the hippocampus (CGH), the fornix, the anterior limb of the internal capsule (ALIC), the posterior limb of the internal capsule (PLIC), and the uncinate fasciculus (UF). We compared diffusion metrics across groups and explored the relationships between diffusion metrics and clinical measures (pain chronicity and intensity, central sensitization, somatization, depression, orofacial behavior severity, jaw function limitations, disability, and interference due to pain) in subjects with TMDs.

Results

We observed differences in diffusion metrics between groups, primarily in the right side of the brain, with the right CGC having lower FA and the right UF having lower FA and higher MD and RD in subjects with TMDs compared to healthy controls. No clinical measures were consistently associated with diffusion metrics in subjects with TMDs.

Conclusion

The UF showed potential microstructural damage in subjects with TMDs, but further studies are needed to confirm any associations between diffusion changes and clinical measures.

Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study

Objective

Acupuncture has been shown to be effective in the treatment of chronic pain. However, their neural mechanism underlying the effective acupuncture response to chronic pain is still unclear. We investigated whether metabolic patterns in the pain matrix network might predict acupuncture therapy responses in patients with primary dysmenorrhea (PDM) using a machine-learning-based multivariate pattern analysis (MVPA) on positron emission tomography data (PET).

Methods

Forty-two patients with PDM were selected and randomized into two groups: real acupuncture and sham acupuncture (three menstrual cycles). Brain metabolic data from the three special brain networks (the sensorimotor network (SMN), default mode network (DMN), and salience network (SN)) were extracted at the individual level by using PETSurfer in fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG-PET) data. MVPA analysis based on metabolic network features was employed to predict the pain relief after treatment in the pooled group and real acupuncture treatment, separately.

Results

Paired t-tests revealed significant alterations in pain intensity after real but not sham acupuncture treatment. Traditional mass-univariate correlations between brain metabolic and alterations in pain intensity were not significant. The MVPA results showed that the brain metabolic pattern in the DMN and SMN did predict the pain relief in the pooled group of patients with PDM (R2 = 0.25, p = 0.005). In addition, the metabolic pattern in the DMN could predict the pain relief after treatment in the real acupuncture treatment group (R2 = 0.40, p = 0.01).

Conclusion

This study indicates that the individual-level metabolic patterns in DMN is associated with real acupuncture treatment response in chronic pain. The present findings advanced the knowledge of the brain mechanism of the acupuncture treatment in chronic pain.

Gray matter volume reduction with different disease duration in trigeminal neuralgia

PURPOSE

Structural magnetic resonance imaging is widely used to explore brain gray and white matter structure in trigeminal neuralgia (TN) but has yielded conflicting findings. This study investigated the relationship between disease duration as a clinical feature of TN and changes in brain structure.

METHODS

We divided 49 TN patients into three groups (TN1-TN3) based on disease duration (TN1 = 1.1 ± 0.7 (0-2) years, TN2 = 4.8 ± 1.5 (3-7) years, TN3 = 15.1 ± 5.5 (10-30) years). We used voxel-based morphometry (VBM) to compare the gray matter volume (GMV) across groups and between TN patients and 18 matched healthy control subjects.

RESULTS

The TN1 group showed reduced GMV of pain-related regions in the cerebellum; the TN2 group showed reduced GMV in the thalamus and the motor/sensory cortex; and the TN3 group showed reduced GMV in the emotional and reward circuits compared with healthy controls. Similar brain regions, including bilateral hippocampi, caudate, left insular cortex, and medial superior frontal cortex, were affected in TN2 and TN3 compared with TN1.

CONCLUSION

Disease duration can explain differences in structural alterations-especially in pain-related brain regions-in TN. These results highlight the advanced structural neuroimaging method that are valuable tools to assess the trigeminal system in TN and may further our current understanding of TN pathology.

Brainstem trigeminal fiber microstructural abnormalities are associated with treatment response across subtypes of trigeminal neuralgia

ABSTRACT

Neurosurgical treatments for trigeminal neuralgia (TN) can provide long-lasting pain relief; however, some patients fail to respond and undergo multiple, repeat procedures. Surgical outcomes can vary depending on the type of TN, but the reasons for this are not well understood. Neuroimaging studies of TN point to abnormalities in the brainstem trigeminal fibers; however, whether this is a common characteristic of treatment nonresponse across different subtypes of TN is unknown. Here, we used diffusion tensor imaging (DTI) to determine whether the brainstem trigeminal fiber microstructure is a common biomarker of surgical response in TN and whether the extent of these abnormalities is associated with the likelihood of response across subtypes of TN. We studied 98 patients with TN (61 classical TN, 26 TN secondary to multiple sclerosis, and 11 TN associated with a solitary pontine lesion) who underwent neurosurgical treatment and 50 healthy controls. We assessed treatment response using pain intensity measures and examined microstructural features by extracting pretreatment DTI metrics from the proximal pontine segment of the trigeminal nerves. We found that microstructural abnormalities in the affected pontine trigeminal fibers (notably, lower fractional anisotropy and higher radial diffusivity) highlight treatment nonresponders (n = 47) compared with responders (n = 51) and controls, and that the degree of abnormalities is associated with the likelihood of surgical response across subtypes of TN. These novel findings demonstrate the value of DTI as an objective, noninvasive tool for the prediction of treatment response and elucidate the features that distinguish treatment responders from nonresponders in the TN population.

Regional brain morphology predicts pain relief in trigeminal neuralgia

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Regional brain gray matter morphology robustly predict TN radiosurgery response.

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Surface area ML model was 96.7% accurate, 100.0% sensitive, and 89.1% specific.

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Top predictor for surface area model was contralateral superior frontal gyrus.

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Cortical thickness ML model was 90.5% accurate, 93.5% sensitive, and 83.7% specific.

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Top predictor for cortical thickness model was contralateral isthmus cingulate gyrus.

Background

Trigeminal 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.

Purpose

To determine if pre-treatment regional brain morphology-based machine learning models can prognosticate 1 year response to Gamma Knife radiosurgery for trigeminal neuralgia.

Methods

We 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.

Results

In 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.

Conclusions

Our 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.

Trigeminal neuralgia diffusivities using Gaussian process classification and merged group tractography

ABSTRACT

Imaging of trigeminal neuralgia (TN) has demonstrated key diffusion tensor imaging-based diffusivity alterations in the trigeminal nerve; however, imaging has primarily focused on the peripheral nerve segment because of previous limitations in reliably segmenting small fiber bundles across multiple subjects. We used Selective Automated Group Integrated Tractography to study 36 subjects with TN (right-sided pain) and 36 sex-matched controls to examine the trigeminal nerve (fifth cranial nerve [CN V]), pontine decussation (TPT), and thalamocortical fibers (S1). Gaussian process classifiers were trained by scrolling a moving window over CN V, TPT, and S1 tractography centroids. Fractional anisotropy (FA), generalized FA, radial diffusivity, axial diffusivity, and mean diffusivity metrics were evaluated for both groups, analyzing TN vs control groups and affected vs unaffected sides. Classifiers that performed at greater-than-or-equal-to 70% accuracy were included. Gaussian process classifier consistently demonstrated bilateral trigeminal changes, differentiating them from controls with an accuracy of 80%. Affected and unaffected sides could be differentiated from each other with 75% accuracy. Bilateral TPT could be distinguished from controls with at least 85% accuracy. TPT left-right classification achieved 98% accuracy. Bilateral S1 could be differentiated from controls, where the affected S1 radial diffusivity classifier achieved 87% accuracy. This is the first TN study that combines group-wise merged tractography, machine learning classification, and analysis of the complete trigeminal pathways from the peripheral fibers to S1 cortex. This analysis demonstrates that TN is characterized by bilateral abnormalities throughout the trigeminal pathway compared with controls and abnormalities between affected and unaffected sides. This full pathway tractography study of TN demonstrates bilateral changes throughout the trigeminal pathway and changes between affected and unaffected sides.

Sex differences in brain modular organization in chronic pain

ABSTRACT

Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.

Current Understanding of the Involvement of the Insular Cortex in Neuropathic Pain: A Narrative Review

Neuropathic pain is difficult to cure and is often accompanied by emotional and psychological changes. Exploring the mechanisms underlying neuropathic pain will help to identify a better treatment for this condition. The insular cortex is an important information integration center. Numerous imaging studies have documented increased activity of the insular cortex in the presence of neuropathic pain; however, the specific role of this region remains controversial. Early studies suggested that the insular lobe is mainly involved in the processing of the emotional motivation dimension of pain. However, increasing evidence suggests that the role of the insular cortex is more complex and may even be related to the neural plasticity, cognitive evaluation, and psychosocial aspects of neuropathic pain. These effects contribute not only to the development of neuropathic pain, but also to its comorbidity with neuropsychiatric diseases. In this review, we summarize the changes that occur in the insular cortex in the presence of neuropathic pain and analgesia, as well as the molecular mechanisms that may underlie these conditions. We also discuss potential sex-based differences in these processes. Further exploration of the involvement of the insular lobe will contribute to the development of new pharmacotherapy and psychotherapy treatments for neuropathic pain.

On the Relationship Between White Matter Structure and Subjective Pain. Lessons From an Acute Surgical Pain Model

Background: Pain has been associated with structural changes of the brain. However, evidence regarding white matter changes in response to acute pain protocols is still scarce. In the present study, we assess the existence of differences in brain white matter related to pain intensity reported by patients undergoing surgical removal of a mandibular impacted third molar using diffusion tensor imaging (DTI) analysis.

Methods: 30 participants reported their subjective pain using a visual analog scale at three postsurgical stages: under anesthesia, in pain, and after the administration of an analgesic. The diffusion data were acquired prior to surgery.

Results: DTI analysis yielded significant positive associations of fractional anisotropy in white matter areas related to pain processing (corticospinal tract, corona radiata, corpus callosum) with the differences in pain between the three postsurgery stages. Extent and location of these associations depended on the magnitude of the subjective pain differences. Tractography analysis indicated that some pain–tract associations are significant only when pain stage is involved in the contrast (posterior corona radiata), while others (middle cerebellar peduncle, pontine crossing) are only when anesthesia is involved in the contrast.

Conclusions: The association of white matter fractional anisotropy and connectivity, measured before the pain stages, with subjective pain depends on the magnitude of the differences in pain scores.

A neuroimaging marker for predicting longitudinal changes in pain intensity of subacute back pain based on large-scale brain network interactions

Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. Although a single connection between the medial prefrontal cortex and nucleus accumbens has been suggested as a powerful marker, how the complex interactions on a large-scale brain network can serve as the markers is underexplored. Here, we aimed to identify a set of functional connections predictive of longitudinal changes in pain intensity using large-scale brain networks. We re-analyzed previously published resting-state functional magnetic resonance imaging data of 49 subacute back pain (SBP) patients. We built a network-level model that predicts changes in pain intensity over one year by combining independent component analysis and a penalized regression framework. Connections involving top-down pain modulation, multisensory integration, and mesocorticolimbic circuits were identified as predictive markers for pain intensity changes. Pearson's correlations between actual and predicted pain scores were r = 0.33-0.72, and group classification results between SBP patients with persisting pain and recovering patients, in terms of area under the curve (AUC), were 0.89/0.75/0.75 for visits four/three/two, thus outperforming the previous work (AUC 0.83/0.73/0.67). This study identified functional connections important for longitudinal changes in pain intensity in SBP patients, providing provisional markers to predict future pain using large-scale brain networks.

Advances in diagnosis, classification, pathophysiology, and management of trigeminal neuralgia

Trigeminal neuralgia is a very painful neurological condition with severe, stimulus-evoked, short-lasting stabbing pain attacks in the face. The past decade has offered new insights into trigeminal neuralgia symptomatology, pathophysiology, and treatment, leading to a change in the classification of the condition. An accurate diagnosis is crucial because neuroimaging interpretation and clinical management differ among the various forms of facial pain. MRI using specific sequences should be a part of the diagnostic workup to detect a possible neurovascular contact and exclude secondary causes. Demonstration of a neurovascular contact should not be used to confirm a diagnosis but rather to facilitate surgical decision making. Carbamazepine and oxcarbazepine are drugs of first choice for long-term treatment, whereas microvascular decompression is the first-line surgery in medically refractory patients. Advances in neuroimaging techniques and animal models will provide further insight into the causes of trigeminal neuralgia and its pathophysiology. Development of more efficacious treatment options is highly warranted.

Temporal disconnection between pain relief and trigeminal nerve microstructural changes after Gamma Knife radiosurgery for trigeminal neuralgia

OBJECTIVE

Gamma Knife radiosurgery (GKRS) is a noninvasive surgical treatment option for patients with medically refractive classic trigeminal neuralgia (TN). The long-term microstructural consequences of radiosurgery and their association with pain relief remain unclear. To better understand this topic, the authors used diffusion tensor imaging (DTI) to characterize the effects of GKRS on trigeminal nerve microstructure over multiple posttreatment time points.

METHODS

Ninety-two sets of 3-T anatomical and diffusion-weighted MR images from 55 patients with TN treated by GKRS were divided within 6-, 12-, and 24-month posttreatment time points into responder and nonresponder subgroups (≥ 75% and < 75% reduction in posttreatment pain intensity, respectively). Within each subgroup, posttreatment pain intensity was then assessed against pretreatment levels and followed by DTI metric analyses, contrasting treated and contralateral control nerves to identify specific biomarkers of successful pain relief.

RESULTS

GKRS resulted in successful pain relief that was accompanied by asynchronous reductions in fractional anisotropy (FA), which maximized 24 months after treatment. While GKRS responders demonstrated significantly reduced FA within the radiosurgery target 12 and 24 months posttreatment (p < 0.05 and p < 0.01, respectively), nonresponders had statistically indistinguishable DTI metrics between nerve types at each time point.

CONCLUSIONS

Ultimately, this study serves as the first step toward an improved understanding of the long-term microstructural effect of radiosurgery on TN. Given that FA reductions remained specific to responders and were absent in nonresponders up to 24 months posttreatment, FA changes have the potential of serving as temporally consistent biomarkers of optimal pain relief following radiosurgical treatment for classic TN.

Diffusion tensor imaging reveals microstructural differences between subtypes of trigeminal neuralgia

OBJECTIVE

Trigeminal neuralgia (TN) is an uncommon idiopathic facial pain syndrome. To assist in diagnosis, treatment, and research, TN is often classified as type 1 (TN1) when pain is primarily paroxysmal and episodic or type 2 (TN2) when pain is primarily constant in character. Recently, diffusion tensor imaging (DTI) has revealed microstructural changes in the symptomatic trigeminal root and root entry zone of patients with unilateral TN. In this study, the authors explored the differences in DTI parameters between subcategories of TN, specifically TN1 and TN2, in the pontine segment of the trigeminal tract.

METHODS

The authors enrolled 8 patients with unilateral TN1, 7 patients with unilateral TN2, and 23 asymptomatic controls. Patients underwent DTI with parameter measurements in a region of interest within the pontine segment of the trigeminal tract. DTI parameters were compared between groups.

RESULTS

In the pontine segment, the radial diffusivity (p = 0.0049) and apparent diffusion coefficient (p = 0.023) values in TN1 patients were increased compared to the values in TN2 patients and controls. The DTI measures in TN2 were not statistically significant from those in controls. When comparing the symptomatic to asymptomatic sides in TN1 patients, radial diffusivity was increased (p = 0.025) and fractional anisotropy was decreased (p = 0.044) in the symptomatic sides. The apparent diffusion coefficient was increased, with a trend toward statistical significance (p = 0.066).

CONCLUSIONS

Noninvasive DTI analysis of patients with TN may lead to improved diagnosis of TN subtypes (e.g., TN1 and TN2) and improve patient selection for surgical intervention. DTI measurements may also provide insights into prognosis after intervention, as TN1 patients are known to have better surgical outcomes than TN2 patients.

Imaging vs quantitative sensory testing to predict chronic pain treatment outcomes

In this article, I review the concept of personalized pain management and consider how brain imaging and quantitative sensory testing can be used to derive biomarkers of chronic pain treatment outcome. I review how different modalities of brain imaging can be used to acquire information about brain structure and function and how this information can be linked to individual measures of pain.

Grey matter volume alterations in trigeminal neuralgia: A systematic review and meta-analysis of voxel-based morphometry studies

In recent decades, a growing number of structural neuroimaging studies of grey matter (GM) in trigeminal neuralgia (TN) have reported inconsistent alterations. We carried out a systematic review and meta-analysis to identify consistent and replicable GM volume abnormalities using effect-size signed differential mapping (ES-SDM). Furthermore, we conducted a meta-regression to explore the potential effects of clinical characteristics on GM volume alterations in patients with TN. A total of 13 studies with 15 datasets, representing 407 TN patients and 376 healthy individuals, were included in the present study. The results revealed that TN patients had GM volume abnormalities mainly in the basal ganglia, including the putamen, nucleus accumbens (NAc), caudate nucleus and amygdala, as well as the cingulate cortex (CC), thalamus, insula and superior temporal gyrus (STG). The meta-regression analysis showed that verbal rating scale (VRS) scores were negatively correlated with decreased GM volume in the left striatum and that illness duration was negatively correlated with decreased GM volume in the left STG and left insula. These results provide a thorough profile of GM volume alterations in TN patients and constitute robust evidence that aberrant GM volumes in the brain regions regulating and moderating sensory-motor and affective processing may play an important role in the pathophysiology of TN.

Primer on machine learning: utilization of large data set analyses to individualize pain management

PURPOSE OF REVIEW

Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both structured and unstructured datasets.

RECENT FINDINGS

Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data used in pain research.

SUMMARY

In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research and clinical settings.

Topography of the pain in classical trigeminal neuralgia: insights into somatotopic organization

Trigeminal neuralgia is defined by its clinical characteristics of paroxysmal unilateral facial pain in a well-defined territory. Distribution of the pain may be in one or several of the cutaneous and/or mucous territories of the three divisions with V2 pain being the most frequent territory followed by V3 and V1. Factors determining the distribution of pain have not yet been systematically investigated. It is now well recognized that vascular compression factor is a predominant aetiology of classical trigeminal neuralgia. In this study we aimed to find whether there is a relation between the location of the vascular compression and the peripheral distribution of the pain. Patients with classical trigeminal neuralgia in whom microvascular decompression was performed were included. Data recorded pertained to the nature of the conflict, its degree and, most importantly, location around the root: supero-median, supero-lateral or inferior. Equally, clinical data for the distribution of pain were recorded. Most of the patients 318 (89.3%) had the compression coming from above, i.e. 220 (61.7%) had compression from a supero-medial direction and 98 (27.5%) from a supero-lateral direction; inferior compression was present in 38 patients (10.7%). Distribution of the pain was significantly different according to the location of the conflict (P = 0.0005, Fisher Exact test). Odds ratios were computed for each location of compression and painful territory involved. According to the overall distribution of pain, patients with supero-medial compression had an odds ratio of 2.7 [95% confidence interval (CI) 1.66–4.41] of manifesting with V1 pain. Conversely V3 pain was less likely to occur with supero-median compression than the other types of pain (odds ratio 0.53, 95% CI 0.34–0.83). Inferior compression on the other hand was more likely to manifest with V3 pain with an odds ratio of 2.56 (95% CI 1.21–5.45). Overall V2 pain had an odds ratio close to 1 regardless of the type of compression. These findings suggest an association between the location of the neurovascular conflict with its resulting insult and the distribution of pain supporting a somatotopic view of the organization of the trigeminal root and a role of the conflict in the clinical manifestation of trigeminal neuralgia.

Neuroimaging-based pain biomarkers: definitions, clinical and research applications, and evaluation frameworks to achieve personalized pain medicine

One of the key ambitions of neuroimaging-based pain biomarker research is to augment patient and clinician reporting of clinically relevant phenomena with neural measures for prediction, prognosis, and detection of pain. Despite years of productive research on the neuroimaging of pain, such applications have seen little advancement. However, recent developments in identifying brain-based biomarkers of pain through advances in technology and multivariate pattern analysis provide some optimism. Here, we (1) define and review the different types of potential neuroimaging-based biomarkers, their clinical and research applications, and their limitations and (2) describe frameworks for evaluation of pain biomarkers used in other fields (eg, genetics, cancer, cardiovascular disease, immune system disorders, and rare diseases) to achieve broad clinical and research utility and minimize the risks of misapplication of this emerging technology. To conclude, we discuss future directions for neuroimaging-based biomarker research to achieve the goal of personalized pain medicine.

Trigeminal nerve and white matter brain abnormalities in chronic orofacial pain disorders

Medial temporal lobe activity is investigated in meta-analyses of experimental and chronic pain. Abnormal hippocampal connectivity is found in patients with chronic low back pain.

The orofacial region is psychologically important, given that it serves fundamental and important biological purposes. Chronic orofacial pain disorders affect the head and neck region. Although some have clear peripheral etiologies, eg, classic trigeminal neuralgia, others do not have a clear etiology (eg, muscular temporomandibular disorders). However, these disorders provide a unique opportunity in terms of elucidating the neural mechanisms of these chronic pain conditions: both the peripheral and central nervous systems can be simultaneously imaged. Diffusion-weighted imaging and diffusion tensor imaging have provided a method to essentially perform in vivo white matter dissections in humans, and to elucidate abnormal structure related to clinical correlates in disorders, such as chronic orofacial pains. Notably, the trigeminal nerve anatomy and architecture can be captured using diffusion imaging. Here, we review the trigeminal somatosensory pathways, diffusion-weighted imaging methods, and how these have contributed to our understanding of the neural mechanisms of chronic pain disorders affecting the trigeminal system. We also discuss novel findings indicating the potential for trigeminal nerve diffusion imaging to develop diagnostic and precision medicine biomarkers for trigeminal neuralgia. In sum, diffusion imaging serves both an important basic science purpose in identifying pain mechanisms, but is also a clinically powerful tool that can be used to improve treatment outcomes.

Neuroimaging-based biomarkers for pain: state of the field and current directions

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.

Selective hippocampal subfield volume reductions in classic trigeminal neuralgia

Trigeminal Neuralgia (TN) is a chronic neuropathic pain syndrome characterized by paroxysmal unilateral shock-like pains in the trigeminal territory most frequently attributed to neurovascular compression of the trigeminal nerve at its root entry zone. Recent advances in the study of TN suggest a possible central nervous system (CNS) role in modulation and maintenance of pain. TN and other chronic pain patients commonly experience alterations in cognition and affect, as well as abnormalities in CNS volume and microstructure in regions associated with pain perception, emotional modulation, and memory consolidation. However, the microstructural changes in the hippocampus, an important structure within the limbic system, have not been previously studied in TN patients. Here, we use grey matter analysis to assess whether TN pain is associated with altered hippocampal subfield volume in patients with classic TN. Anatomical magnetic resonance (MR) images of twenty-two right-sided TN patients and matched healthy controls underwent automated segmentation of hippocampal subfields using FreeSurfer v6.0. Right-sided TN patients had significant volumetric reductions in ipsilateral cornu ammois 1 (CA1), CA4, dentate gyrus, molecular layer, and hippocampus-amygdala transition area - resulting in decreased whole ipsilateral hippocampal volume, compared to healthy controls. Overall, we demonstrate selective hippocampal subfield volume reduction in patients with classic TN. These changes occur in subfields implicated as neural circuits for chronic pain processing. Selective subfield volume reduction suggests aberrant processes and circuitry reorganization, which may contribute to development and/or maintenance of TN symptoms.