Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study.

OBJECTIVE: This study assesses the concordance in migraine diagnosis between an online, self-administered, Computer-based, Diagnostic Engine (CDE) and semi-structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD-3) criteria. BACKGROUND: Delay in accurate diagnosis is a major barrier to headache care. Accurate computer-based algorithms may help reduce the need for SSI-based encounters to arrive at correct ICHD-3 diagnosis. METHODS: Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross-sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web-based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web-based questionnaire or the web-based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen's kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard. RESULTS: Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28-40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75-0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%-94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%-99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%-99.0%) and 86.6% (95% CI: 79.3%-91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%-87.8%) and 98.9% (95% CI: 98.1%-99.3%), respectively. CONCLUSION: The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis.

Three Dimensions of Association Link Migraine Symptoms and Functional Connectivity.

Migraine is a heterogeneous disorder with variable symptoms and responsiveness to therapy. Because of previous analytic shortcomings, variance in migraine symptoms has been inconsistently related to brain function. In the current analysis, we used data from two sites (n = 143, male and female humans), and performed canonical correlation analysis, relating resting-state functional connectivity (RSFC) with a broad range of migraine symptoms, ranging from headache characteristics to sleep abnormalities. This identified three dimensions of covariance between symptoms and RSFC. The first dimension related to headache intensity, headache frequency, pain catastrophizing, affect, sleep disturbances, and somatic abnormalities, and was associated with frontoparietal and dorsal attention network connectivity, both of which are major cognitive networks. Additionally, RSFC scores from this dimension, both the baseline value and the change from baseline to postintervention, were associated with responsiveness to mind-body therapy. The second dimension was related to an inverse association between pain and anxiety, and to default mode network connectivity. The final dimension was related to pain catastrophizing, and salience, sensorimotor, and default mode network connectivity. In addition to performing canonical correlation analysis, we evaluated the current clustering of migraine patients into episodic and chronic subtypes, and found no evidence to support this clustering. However, when using RSFC scores from the three significant dimensions, we identified a novel clustering of migraine patients into four biotypes with unique functional connectivity patterns. These findings provide new insight into individual variability in migraine, and could serve as the foundation for novel therapies that take advantage of migraine heterogeneity.SIGNIFICANCE STATEMENT Using a large multisite dataset of migraine patients, we identified three dimensions of multivariate association between symptoms and functional connectivity. This analysis revealed neural networks that relate to all measured symptoms, but also to specific symptom ensembles, such as patient propensity to catastrophize painful events. Using these three dimensions, we found four biotypes of migraine informed by clinical and neural variation together. Such findings pave the way for precision medicine therapy for migraine.

Endogenous glucocorticoids may serve as biomarkers for migraine chronification.

AIMS: The aims of this study were to: (a) identify differences in serum and cerebrospinal fluid (CSF) glucocorticoids among episodic migraine (EM) and chronic migraine (CM) patients compared with controls; (b) determine longitudinal changes in serum glucocorticoids in CM patients; and (c) determine migraine-related clinical features contributing to glucocorticoid levels. METHODS: Serum and CSF levels of cortisol and corticosterone were measured using liquid chromatography-mass spectrometry among adult patients with EM, CM, and controls. Serum and CSF samples were collected from 26 and four participants in each group, respectively. Serum glucocorticoids were measured at a second timepoint after 2 years among 10 of the CM patients, six of whom reverted to EM while four persisted as CM. Receiver operating characteristic (ROC) analysis was made to assess the migraine diagnostic performance of glucocorticoids. Regression analysis was conducted to determine the link between glucocorticoid levels and migraine-related clinical variables. RESULTS: CM patients exhibited significantly elevated serum and CSF levels of cortisol and corticosterone compared with controls and EM patients (age, sex, body mass index adjusted; Kruskal-Wallis p < 0.05). ROC showed area-under-curve of 0.89 to differentiate CM from EM. CM patients with remission had their serum glucocorticoids return to control or near EM levels (p < 0.05). Persistent CM showed unremitting serum glucocorticoids. Migraine frequency and disability contributed to increased cortisol, while pain self-efficacy predicted lower cortisol levels (p < 0.005). CONCLUSION: Endogenous glucocorticoids may be biomarkers for migraine progression and for monitoring treatment response. Improving pain self-efficacy skills may help optimize endogenous glucocorticoid levels, which in turn may prevent migraine attacks.

Exploring Natural Clusters of Chronic Migraine Phenotypes: A Cross-Sectional Clinical Study.

Heterogeneity in chronic migraine (CM) presents significant challenge for diagnosis, management, and clinical trials. To explore naturally occurring clusters of CM, we utilized data reduction methods on migraine-related clinical dataset. Hierarchical agglomerative clustering and principal component analyses (PCA) were conducted to identify natural clusters in 100 CM patients using 14 migraine-related clinical variables. Three major clusters were identified. Cluster I (29 patients) - the severely impacted patient featured highest levels of depression and migraine-related disability. Cluster II (28 patients) - the minimally impacted patient exhibited highest levels of self-efficacy and exercise. Cluster III (43 patients) - the moderately impacted patient showed features ranging between Cluster I and II. The first 5 principal components (PC) of the PCA explained 65% of variability. The first PC (eigenvalue 4.2) showed one major pattern of clinical features positively loaded by migraine-related disability, depression, poor sleep quality, somatic symptoms, post-traumatic stress disorder, being overweight and negatively loaded by pain self-efficacy and exercise levels. CM patients can be classified into three naturally-occurring clusters. Patients with high self-efficacy and exercise levels had lower migraine-related disability, depression, sleep quality, and somatic symptoms. These results may ultimately inform different management strategies.

Altered structural brain network topology in chronic migraine.

Despite its prevalence and high disease burden, the pathophysiological mechanisms underlying chronic migraine (CM) are not well understood. As CM is a complex disorder associated with a range of sensory, cognitive, and affective comorbidities, examining structural network disruption may provide additional insights into CM symptomology beyond studies of focal brain regions. Here, we compared structural interconnections in patients with CM (n = 52) and healthy controls (HC) (n = 48) using MRI measures of cortical thickness and subcortical volume combined with graph theoretical network analyses. The analysis focused on both local (nodal) and global measures of topology to examine network integration, efficiency, centrality, and segregation. Our results indicated that patients with CM had altered global network properties that were characterized as less integrated and efficient (lower global and local efficiency) and more highly segregated (higher transitivity). Patients also demonstrated aberrant local network topology that was less integrated (higher path length), less central (lower closeness centrality), less efficient (lower local efficiency) and less segregated (lower clustering). These network differences not only were most prominent in the limbic and insular cortices but also occurred in frontal, temporal, and brainstem regions, and occurred in the absence of group differences in focal brain regions. Taken together, examining structural correlations between brain areas may be a more sensitive means to detect altered brain structure and understand CM symptomology at the network level. These findings contribute to an increased understanding of structural connectivity in CM and provide a novel approach to potentially track and predict the progression of migraine disorders.This study is registered on ClinicalTrials.gov (Identifier: NCT03304886).

Clinical Features Contributing to Cortical Thickness Changes in Chronic Migraine - A Pilot Study.

OBJECTIVES: The objectives of this cross-sectional pilot study were threefold: to identify regions of cortical thickness that differentiate chronic migraine (CM) from controls, to assess group differences in interregional cortical thickness covariance, and to determine group differences in associations between clinical variables and cortical thickness. BACKGROUND: Cortical thickness alterations in relation to clinical features have not been adequately explored in CM. Assessment of this relationship can be useful to describe cortical substrates for disease progression in migraine and to identify clinical variables that warrant management emphasis. METHODS: Thirty CM cases (mean age 40 years; male-to-female 1:4) and 30 sex-matched healthy controls (mean age 40 years) were enrolled. Participants completed self-administered and standardized questionnaires assessing headache-related clinical features and common psychological comorbidities. T1-weighted brain images were acquired on a 3T MRI. A whole-brain cortical thickness analysis was performed. Additionally, correlations between all brain regions were assessed to examine interregional cortical thickness covariance. Interactions were analyzed to identify clinical variables that were significantly associated with cortical thickness. RESULTS: The whole brain cortical thickness analysis revealed no significant differences between CM patients and controls. However, significant associations between clinical features and cortical thickness were observed for the patients only. These associations included the right superior temporal sulcus (R2  = 0.72, P = .001) and the right insula (R2  = 0.71, P = .002) with distinct clinical variables ie, longer history of CM, posttraumatic stress disorder (PTSD), sleep quality, pain self-efficacy, and somatic symptoms. Higher interregional cortical covariance was found in CM compared to controls (OR = 3.1, CI 2.10-4.56, P < .0001), such that cortical thickness between regions tended to be more correlated in patients, particularly in the temporal and frontal lobes. CONCLUSION: CM patients have significantly greater cortical covariance compared to controls. Cortical thickness in CM patients was predominantly accounted for by CM duration, PTSD, and poor sleep quality, while improved pain self-efficacy buffered cortical thickness. While it is important to address all CM features and comorbidities, it may be useful to emphasize optimizing the management of certain clinical features that contribute to cortical abnormalities including managing PTSD, early management to shorten duration of CM, and improving pain self-efficacy and sleep quality.