Tension-type headache (TTH) and migraine are two common primary headaches distinguished by clinical characteristics according to the 3^rd edition of the International Classification of Headache Disorders. Migraine is identified by specific features such as being more prevalent in females, being aggravated by physical activity, certain genetic factors, having photophobia, phonophobia, nausea, vomiting, or aura, and responding to specific drugs. Nonetheless, TTH and migraine share some common characteristics, such as onset occurring in the 20 s, and being triggered by psychological factors like stress, moderate pain severity, and mild nausea in chronic TTH. Both conditions involve the trigeminovascular system in their pathophysiology. However, distinguishing between TTH and migraine in clinical practice, research, and epidemiological studies can be challenging, as there is a lack of specific diagnostic tests and biomarkers. Moreover, both conditions may coexist, further complicating the diagnostic process. This review aims to explore the similarities and differences in the pathophysiology, epidemiology, burden and disability, comorbidities, and responses to pharmacological and non-pharmacological treatments of TTH and migraine. The review also discusses future research directions to address the diagnostic challenges and improve the understanding and management of these conditions. Graphical Abstract

Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application.

Background and Objectives: The diagnostic value of thrombophilia remains unknown in young patients with patent foramen ovale (PFO) and stroke. In this study we hypothesized that inherited thrombophilias that lead to venous thrombosis are more prevalent in patients with PFO. Materials and Methods: The study included patients of the tertiary center Vilnius University Hospital Santaros Klinikos who had a cryptogenic ischemic stroke between the ages of 18 and 50 between the years 2008 and 2021. Transient ischemic attacks were excluded. Contrast-enhanced transcranial Doppler ultrasound and extensive laboratory testing were performed. Results: The study included 161 cryptogenic stroke patients (mean age 39.2 ± 7.6 years; 54% female), and a right-to-left shunt was found in 112 (69.6%). The mean time between stroke and thrombophilia testing was 210 days (median 98 days). In total, 61 (39.8%) patients were diagnosed with thrombophilia. The most common finding was hyperhomocysteinemia (26.7%), 14.3% of which were genetically confirmed. Two patients (1.2%) were diagnosed with factor V Leiden mutation, three patients (1.9%) with prothrombin G20210A mutation, one patient (0.6%) had a protein C mutation and one patient (0.6%) had a protein S mutation. No antithrombin mutations were diagnosed in our study population. A total of 45.5% of patients with inherited thrombophilia had a right-to-left shunt, while 54.5% did not, p = 0.092. Personal thrombosis anamnesis was positive significantly more often in patients with antiphospholipid syndrome. Conclusions: The hypothesis of the study was rejected since inherited venous thrombophilia was not significantly more common in patients with PFO. Due to the rarity of thrombophilias in general, more research with a larger sample size is required to further verify our findings.

Background: Despite wearable sensors’ ability to provide continuous physiologic monitoring, migraine remains challenging to predict due to unpredictability of onset and a variety of triggers. Developing an accurate prediction model requires reducing signal variability by using effective filtering techniques. Objective: The main objective of this study is to evaluate machine learning models for predicting migraines and analyze the effect of different filtering techniques and classifiers on prediction performance. Methods: A feature set based on ANOVA analysis of four key physiological signals was used. After the pre-processing, filtering methods, including median, Butterworth, and Savitzky-Golay filter, were applied. Five classification models, Extreme Gradient Boosting, Histogram-Based Gradient Boosting, Random Forest, Support Vector Machine, and K-Nearest Neighbors, were evaluated. Results: The highest predictive performance was achieved using the Savitzky-Golay filter. The Random Forest model demonstrated the best accuracy (0.858) and precision (0.815), and an F1-score of 0.677, indicating the potential of investigated signals for migraine prediction. Furthermore, the Histogram-Based Gradient Boosting model achieved the highest recall using the Savitzky-Golay filter (0.719), demonstrating its effectiveness in identifying true positive cases of migraines. Conclusion: The results indicate significant potential for healthcare applications for early migraine prediction and treatment using wearable technology. The study highlights the importance of selecting appropriate features and filtering methods to improve the accuracy and reliability of the predictions.

Background: Migraine is a complex neurological disorder characterized by recurrent, often debilitating headaches. Current evidence suggests that autonomic nervous system (ANS) alterations play a significant role in migraine pathophysiology, affecting sensory, limbic, and homeostatic processing. Heart rate variability (HRV), a well-established, noninvasive marker of ANS function, is associated with migraine severity and treatment efficacy. Objective: In this study, we aimed to evaluate the use of wearable sensor technology in predicting migraine attacks by monitoring changes in the ANS during the prodrome phase. Methods: We recruited 23 migraine sufferers and analyzed HRV during nocturnal sleep using wearable biosensors and machine learning, extracting HRV features from BVP signals and applying feature engineering to predict migraine episodes. Results: The analysis of HRV provides an important approach to migraine attack prediction, revealing significant individual variability in physiological responses. Overall, these results lay the groundwork for developing more effective and personalized migraine prediction models, which could lead to earlier interventions and improved participant outcomes. Future research should consider controlled inclusion of post-migraine nights, potentially leveraging additional statistical or machine learning techniques to mitigate misclassification risks while capturing these transitional dynamics.