Hippocampus diffusivity abnormalities in classical trigeminal neuralgia.

Introduction: Patients with chronic pain frequently report cognitive symptoms that affect memory and attention, which are functions attributed to the hippocampus. Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder characterized by paroxysmal attacks of unilateral orofacial pain. Given the stereotypical nature of TN pain and lack of negative symptoms including sensory loss, TN provides a unique model to investigate the hippocampal implications of chronic pain. Recent evidence demonstrated that TN is associated with macrostructural hippocampal abnormalities indicated by reduced subfield volumes; however, there is a paucity in our understanding of hippocampal microstructural abnormalities associated with TN. Objectives: To explore diffusivity metrics within the hippocampus, along with its functional and structural subfields, in patients with TN. Methods: To examine hippocampal microstructure, we utilized diffusion tensor imaging in 31 patients with TN and 21 controls. T1-weighted magnetic resonance images were segmented into hippocampal subfields and registered into diffusion-weighted imaging space. Fractional anisotropy (FA) and mean diffusivity were extracted for hippocampal subfields and longitudinal axis segmentations. Results: Patients with TN demonstrated reduced FA in bilateral whole hippocampi and hippocampal body and contralateral subregions CA2/3 and CA4, indicating microstructural hippocampal abnormalities. Notably, patients with TN showed significant correlation between age and hippocampal FA, while controls did not exhibit this correlation. These effects were driven chiefly by female patients with TN. Conclusion: This study demonstrates that TN is associated with microstructural hippocampal abnormalities, which may precede and potentially be temporally linked to volumetric hippocampal alterations demonstrated previously. These findings provide further evidence for the role of the hippocampus in chronic pain and suggest the potential for targeted interventions to mitigate cognitive symptoms in patients with chronic pain.

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.

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.

Differential expression of a brain aging biomarker across discrete chronic pain disorders.

ABSTRACT: Chronic pain has widespread, detrimental effects on the human nervous system and its prevalence and burden increase with age. Machine learning techniques have been applied on brain images to produce statistical models of brain aging. Specifically, the Gaussian process regression is particularly effective at predicting chronological age from neuroimaging data which permits the calculation of a brain age gap estimate (brain-AGE). Pathological biological processes such as chronic pain can influence brain-AGE. Because chronic pain disorders can differ in etiology, severity, pain frequency, and sex-linked prevalence, we hypothesize that the expression of brain-AGE may be pain specific and differ between discrete chronic pain disorders. We built a machine learning model using T1-weighted anatomical MRI from 812 healthy controls to extract brain-AGE for 45 trigeminal neuralgia (TN), 52 osteoarthritis (OA), and 50 chronic low back pain (BP) subjects. False discovery rate corrected Welch t tests were conducted to detect significant differences in brain-AGE between each discrete pain cohort and age-matched and sex-matched controls. Trigeminal neuralgia and OA, but not BP subjects, have significantly larger brain-AGE. Across all 3 pain groups, we observed female-driven elevation in brain-AGE. Furthermore, in TN, a significantly larger brain-AGE is associated with response to Gamma Knife radiosurgery for TN pain and is inversely correlated with the age at diagnosis. As brain-AGE expression differs across distinct pain disorders with a pronounced sex effect for female subjects. Younger women with TN may therefore represent a vulnerable subpopulation requiring expedited chronic pain intervention. To this end, brain-AGE holds promise as an effective biomarker of pain treatment response.

Pain Relief Reverses Hippocampal Abnormalities in Trigeminal Neuralgia.

Chronic pain patients frequently report memory and concentration difficulties. Objective testing in this population points to poor performance on memory and cognitive tests, and increased comorbid anxiety and depression. Recent evidence has suggested convergence between chronic pain and memory deficits onto the hippocampus. The hippocampus consists of heterogenous subfields involved in memory consolidation, behavior regulation, and stress modulation. Despite significant studies outlining hippocampal changes in human and chronic pain animal models, the effect of pain relief on hippocampal abnormalities remains unknown. Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder which is highly amenable to surgical interventions, providing a unique opportunity to investigate the effect of pain relief. This study investigates the effect of pain relief on hippocampal subfields in TN. Anatomical MR images of 61 TN patients were examined before and 6 months after surgery. Treatment responders (n = 47) reported 95% pain relief, whereas non-responders (n = 14) reported 40% change in pain on average. At baseline, patients had smaller hippocampal volumes, compared to controls. After surgery, responders' hippocampal volumes normalized, largely driven by CA2/3, CA4, and dentate gyrus, which are involved in memory consolidation and neurogenesis. We propose that hippocampal atrophy in TN is pain-driven and successful treatment normalizes such abnormalities. PERSPECTIVE: Chronic pain patients have structural abnormalities in the hippocampus and its subfields. Pain relief normalizes these structural abnormalities and impacts patients in a sex-dependent manner.

Standardizing T1-w/T2-w ratio images in trigeminal neuralgia to estimate the degree of demyelination in vivo.

BACKGROUND: Novel magnetic resonance (MR) imaging techniques have led to the development of T1-w/T2-w ratio images or "myelin-sensitive maps (MMs)" to estimate and compare myelin content in vivo. Currently, raw image intensities in conventional MR images are unstandardized, preventing meaningful quantitative comparisons. We propose an improved workflow to standardize the MMs, which was applied to patients with classic trigeminal neuralgia (CTN) and trigeminal neuralgia secondary to multiple sclerosis (MSTN), to assess the validity and feasibility of this clinical tool. METHODS: T1-w and T2-w images were obtained for 17 CTN patients and 17 MSTN patients using a 3 T scanner. Template images were obtained from ICBM152. Multiple sclerosis (MS) plaques in the pons were labelled in MSTN patients. For each patient image, a Gaussian curve was fitted to the histogram of its intensity distribution, and transformed to match the Gaussian curve of its template image. RESULTS: After standardization, the structural contrast of the patient image and its histogram more closely resembled the ICBM152 template. Moreover, there was reduced variability in the histogram peaks of the gray and white matter between patients after standardization (p < 0.001). MM intensities were decreased within MS plaques, compared to normal-appearing white matter (NAWM) in MSTN patients (p < 0.001) and its corresponding regions in CTN patients (p < 0.001). CONCLUSIONS: Images intensities are calibrated according to a mathematic relationship between the intensities of the patient image and its template. Reduced variability among histogram peaks allows for interpretation of tissue-specific intensity and facilitates quantitative analysis. The resultant MMs facilitate comparisons of myelin content between different regions of the brain and between different patients in vivo. MM analysis revealed reduced myelin content in MS plaques compared to its corresponding regions in CTN patients and its surrounding NAWM in MSTN patients. Thus, the standardized MM serves as a non-invasive, easily-automated tool that can be feasibly applied to clinical populations for quantitative analyses of myelin content.

Regional brain morphology predicts pain relief in trigeminal neuralgia.

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.

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.

Trigeminal neuralgia associated with a solitary pontine lesion: clinical and neuroimaging definition of a new syndrome.

Conventional magnetic resonance imaging of patients with trigeminal neuralgia (TN) does not typically reveal associated brain lesions. Here, we identify a unique group of TN patients who present with a single brainstem lesion, who do not fulfill diagnostic criteria for multiple sclerosis (MS). We aim to define this new clinical syndrome, which we term TN associated with solitary pontine lesion (SPL-TN), using a clinical and neuroimaging approach. We identified 24 cases of SPL-TN, 18 of which had clinical follow-up for assessment of treatment response. Lesion mapping was performed to determine the exact location of the lesions and site of maximum overlap across patients. Diffusion tensor imaging was used to assess the white-matter microstructural properties of the lesions. Diffusivity metrics were extracted from the (1) SPL-TN lesions, (2) contralateral, unaffected side, (3) MS brainstem plaques from 17 patients with TN secondary to MS, (4) and healthy controls. We found that 17/18 patients were nonresponders to surgical treatment. The lesions were uniformly located along the affected trigeminal pontine pathway, where the site of maximum overlap across patients was in the area of the trigeminal nucleus. The lesions demonstrated abnormal white-matter microstructure, characterized by lower fractional anisotropy, and higher mean, radial, and axial diffusivities compared with the unaffected side. The brainstem trigeminal fiber microstructure within a lesion highlighted the difference between SPL-TN lesions and MS plaques. In conclusion, SPL-TN patients have identical clinical features to TN but have a single pontine lesion not in keeping with MS and are refractory to surgical management.

Trigeminal neuralgia associated with multiple sclerosis: A multimodal assessment of brainstem plaques and response to Gamma Knife radiosurgery.

BACKGROUND: Gamma Knife radiosurgery (GKRS) is a minimally invasive procedure for trigeminal neuralgia secondary to multiple sclerosis (MS-TN). Patients with MS-TN experience suboptimal response rates to treatment, and the relationship between trigeminal microstructure and treatment outcome is poorly understood. OBJECTIVE: To characterize imaging features of MS-TN pain and GKRS response. METHODS: 3 T diffusion-weighted imaging (DWI), T1-w, T2-w, and fluid-attenuated inversion recovery (FLAIR) sequences were acquired for 18 MS-TN patients undergoing GKRS. Brainstem plaques were standardized into a common space to determine plaque distribution. Ratio of T1-w/T2-w or "myelin maps (MM)" was generated. Multi-tensor tractography was used to delineate the radiosurgical target (RT), root entry zone (REZ), and proximal pontine segment (PPS) of the trigeminal nerves. RESULTS: Laterality of MS-TN is associated with increased axial diffusivity at the PPS, whereas decreased MM at the PPS correlated with poor GKRS response. Preoperatively, GKRS responders have higher fractional anisotropy at the RT, higher axial diffusivity at the REZ, and higher MM intensities at the PPS. CONCLUSION: This study demonstrates that diffusivities and MM intensities are important correlates of pain and treatment response, respectively. Overall, preoperative multimodal assessment of the central trigeminal pathway is a better indicator of GKRS response than postoperative assessment of the reduction in fractional anisotropy peripherally.

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.

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

Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.