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

Increased apathy post-interstitial laser capsulotomy for refractory obsessive-compulsive disorder

BACKGROUND

MRI guided laser interstitial thermal therapy (M-LITT) capsulotomy has proven to be efficacious in decreasing refractory obsessive-compulsive disorder (OCD) related symptomatology yet capsulotomy either via radiosurgery or radiofrequency ablation has in some patients led to increased apathy following surgery. The current case series aims to investigate objective patient-reported change in apathy, disinhibition, depression, and executive dysfunction following anterior capsulotomy via M-LITT for OCD.

METHODS

Ten consecutive patients pre- and post-M-LITT completed measures of OCD, apathy, disinhibition, executive dysfunction, and depression (Mtime between = 1.3 years; 0.42-3.7 years). Reliable Change Index (RCI) was used to evaluate change in pre- and post-M-LITT. OCD symptom response was evaluated using percent change (Y-BOCS scores: 24-34 % reduction indicating partial response; ≥35% reduction indicating full response).

RESULTS

Positive post-surgical change was noted in OCD symptomatology with >65% reporting a partial or full response. However, six patients endorsed increased apathy with half of the non-responders (e.g., less than <24% score reduction on Y-BOCS) reporting increases in apathy. Patients reported relatively stable disinhibition and executive dysfunction, while over half reported a decrease in depression symptoms. Two of the non-responders and one responder endorsed increased apathy despite stable or improved depression symptoms, disinhibition, and executive dysfunction.

CONCLUSIONS

Most patients in the current cohort achieved full-or-partial OCD recovery. Yet, 60% of patients also reported significant increases in apathy, despite experiencing a decrease in depression symptoms, with stable disinhibition and executive dysfunction. Despite these promising improvements in OCD symptomatology following M-LITT, further investigations of the impact of surgery and lesion location on apathy levels is clearly warranted using objective, quantifiable methods.

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.

A novel indicator to predict the outcome of percutaneous stereotactic radiofrequency rhizotomy for trigeminal neuralgia patients: diffusivity metrics of MR-DTI

Magnetic resonance-diffusion tensor imaging (MR-DTI) has been used in the microvascular decompression and gamma knife radiosurgery in trigeminal neuralgia (TN) patients; however, use of percutaneous stereotactic radiofrequency rhizotomy (PSR) to target an abnormal trigeminal ganglion (ab-TG) is unreported. Fractional anisotropy (FA), mean and radial diffusivity (MD and RD, respectively), and axial diffusivity (AD) of the trigeminal nerve (CNV) were measured in 20 TN patients and 40 healthy control participants immediately post PSR, at 6-months, and at 1 year. Longitudinal alteration of the diffusivity metrics and any correlation with treatment effects, or prognoses, were analyzed. In the TN group, either low FA (value < 0.30) or a decreased range compared to the adjacent FA (dFA) > 17% defined an ab-TG. Two-to-three days post PSR, all 15 patients reported decreased pain scores with increased FA at the ab-TG (P < 0.001), but decreased MD and RD (P < 0.01 each). Treatment remained effective in 10 of 14 patients (71.4%) and 8 of 12 patients (66.7%) at the 6-month and 1-year follow-ups, respectively. In patients with ab-TGs, there was a significant difference in treatment outcomes between patients with low FA values (9 of 10; 90%) and patients with dFA (2 of 5; 40%) (P < 0.05). MR-DTI with diffusivity metrics correlated microstructural CNV abnormalities with PSR outcomes. Of all the diffusivity metrics, FA could be considered a novel objective quantitative indicator of treatment effects and a potential indicator of PSR effectiveness in TN patients.

Brain diffusion MRI biomarkers after oncology treatments

In addition to providing a measurement of the tumor's size and dimensions, magnetic resonance imaging (MRI) provides excellent noninvasive radiographic detection of tumor location. The MRI technique is an important modality that has been shown to be useful in the prognosis, diagnosis, treatment planning, and evaluation of response and recurrence in solid cancers. Diffusion-weighted imaging (DWI) is an imaging technique that quantifies water mobility. This imaging approach is good for identifying sub-voxel microstructure of tissues, correlates with tumor cellularity, and has been proven to be valuable in the early assessment of cytotoxic treatment for a variety of malignancies. Diffusion tensor imaging (DTI) is an MRI method that assesses the preferred amount of water transport inside tissues. This enables precise measurements of water diffusion, which changes according to the direction of white matter fibers, their density, and myelination. This measurement corresponds to some related variables: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and others. DTI biomarkers can detect subtle changes in white matter microstructure and integrity following radiation therapy (RT) or chemoradiotherapy, which may have implications for cognitive function and quality of life. In our study, these indices were evaluated after brain chemoradiotherapy.

Beyond biopsychosocial: The keystone mechanism theory of pain

Pain is a deeply personal experience, with interindividual differences in its chronification and treatment presenting a formidable healthcare challenge. The biopsychosocial model (BPSm) has been hugely influential within nascent attempts at precision pain medicine, steering the field away from a reductionist biomechanical viewpoint and emphasising complex interactions of biological, psychological, and social factors which shape the individuality of pain. However, despite offering a strong theoretical foundation and holistic perspective, we contend that the BPSm remains limited in its capacity to deliver truly mechanistically informed treatment of pain. We therefore propose the keystone model of pain which offers a pragmatic balance between the dimensionality expansive BPSm and overly reductive approaches, providing both theoretical and practical advantages for the transition from treating populations to individual people.

Sex differences in patient journeys to diagnosis, referral, and surgical treatment of trigeminal neuralgia: implications for equitable care

OBJECTIVE

Trigeminal neuralgia (TN) is an orofacial pain disorder that is more prevalent in females than males. Although an increasing number of studies point to sex differences in chronic pain, how sex impacts TN patients' journeys to care has not been previously addressed. This study sought to investigate sex differences in patients' journeys to diagnosis, referral, and treatment of TN within a large national context.

METHODS

Patients with classic TN (n = 100; 50 females and 50 males) were randomly selected through chart reviews at the largest surgical treatment center for TN in Canada for a cross-sectional study. Statistical tests, including Welch's t-test, the chi-square test, Pearson's correlations, and analyses of covariance, were conducted with Python.

RESULTS

Key discrepancies between sexes in access to care were identified. Females had a significantly longer referral time interval (average 53.2 months vs 20.4 months, median 27.5 months vs 11.0 months, p = 0.018) and total time interval (average 121.1 months vs 67.8 months, median 78.0 months vs 45.2 months, p = 0.018) than males, despite reporting higher pain intensity at referral. Although medically intolerant patients had a significantly shorter referral time interval than medically tolerant patients (average 13.0 months vs 41.0 months, median 6.0 months vs 17.0 months, p < 0.001), medically tolerant females had a significantly longer referral time interval than medically tolerant males (average 59.9 months vs 21.7 months, median 30.0 months vs 12.0 months, p = 0.017). No statistically significant differences were detected between the sexes for diagnostic time interval (average 63.3 months vs 43.0 months, median 24.0 months vs 24.0 months, p = 0.263) or treatment time interval (average 4.6 months vs 4.7 months, median 4.0 months vs 3.0 months, p = 0.986).

CONCLUSIONS

Critical sex differences in patients' journeys to TN surgical treatment were identified, with females enduring considerably longer referral timelines and expressing significantly greater pain intensity than males at referral. Taken together, our findings suggest the presence of unconscious bias and discrimination against females and highlight the need for expediting TN treatment referral for female TN patients.

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.

Gamma Knife Radiosurgery for Trigeminal Neuralgia : Review and Update

Accurate diagnosis of trigeminal neuralgia (TN) is the starting point for optimal treatment. Gamma knife radiosurgery (GKRS) is currently regarded as one of the first-line treatment options for medically refractory TN. GKRS is a less invasive treatment with a low risk of complications than other surgical procedures that provides a favorable pain control (BNI I–IIIb) rate of >75% at short-term follow-up. Drawbacks of GKRS include the latency period before pain relief and higher recurrence rate compared with microvascular decompression. Therefore, repeat treatment is necessary if the initial GKRS was effective but followed by recurrence. The concept of dose rate and the biologically effective dose of radiation has been actively studied in radiation oncology and is also applied in GKRS for TN to achieve high safety and efficacy by prescribing the optimal dose. Recent progress in functional imaging, such as diffusion tensor imaging, enables us to understand the pathophysiology of TN and predict the clinical outcome after GKRS. Here, we review TN, GKRS, and recent updates, especially in the concepts of radiation dose, diffusion tensor imaging studies, and repeat treatment in GKRS for TN.

Performance of the Hull Salford Cambridge Decision Rule (HSC DR) for early discharge of patients with findings on CT scan of the brain: a CENTER-TBI validation study

BACKGROUND

There is international variation in hospital admission practices for patients with mild traumatic brain injury (TBI) and injuries on CT scan. Only a small proportion of patients require neurosurgical intervention, while many guidelines recommend routine admission of all patients. We aim to validate the Hull Salford Cambridge Decision Rule (HSC DR) and the Brain Injury Guidelines (BIG) criteria to select low-risk patients for discharge from the emergency department.

METHOD

A cohort from 18 countries of Glasgow Coma Scale 13-15 patients with injuries on CT imaging was identified from the multicentre Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) Study (conducted from 2014 to 2017) for secondary analysis. A composite outcome measure encompassing need for ongoing hospital admission was used, including seizure activity, death, intubation, neurosurgical intervention and neurological deterioration. We assessed the performance of our previously derived prognostic model, the HSC DR and the BIG criteria at predicting deterioration in this validation cohort.

RESULTS

Among 1047 patients meeting the inclusion criteria, 267 (26%) deteriorated. Our prognostic model achieved a C-statistic of 0.81 (95% CI: 0.78 to 0.84). The HSC DR achieved a sensitivity of 100% (95% CI: 97% to 100%) and specificity of only 4.7% (95% CI: 3.3% to 6.5%) for deterioration. Using the BIG criteria for discharge from the ED achieved a higher specificity (13.3%, 95% CI: 10.9% to 16.1%) and lower sensitivity (94.6%, 95% CI: 90.5% to 97%), with 12/105 patients recommended for discharge subsequently deteriorating, compared with 0/34 with the HSC DR.

CONCLUSION

Our decision rule would have allowed 3.5% of patients to be discharged, none of whom would have deteriorated. Use of the BIG criteria may select patients for discharge who have too high a risk of subsequent deterioration to be used clinically. Further validation and implementation studies are required to support use in clinical practice.

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

•

Regional brain gray matter morphology robustly predict TN radiosurgery response.

•

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

•

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

•

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

•

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.