Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs’ management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine.

The introduction of monoclonal antibodies (mAbs) directed against the calcitonin gene-related peptide (CGRP), or its receptor (CGRPr), revolutionized migraine management due to their high efficacy and few side effects. Data suggest that the CGRP may even be implicated in circadian rhythm, but studies about the effect of anti-CGRP treatments on sleep are still lacking. The aim of the present study was to assess the effect of erenumab (70 and 140 mg per month), a human mAb directed against CGRPr, on chronotype in chronic migraineurs; secondly, we assessed its efficacy, safety, and the effects on anxiety and depression. Sleep was evaluated using self-administrable questionnaires investigating chronotype, sleep quality, and daytime sleepiness. Migraine diaries and several self-administrable questionnaires regarding headache impact and psychological correlates were evaluated every 3 months during 12 months of treatment. Eighty-eight patients were included; most of them showed a significant reduction in headache frequency and an improvement in psychological symptoms. Moreover, an initial change in chronotype was observed at the three-month assessment from a morning chronotype to an intermediate one; a similar trend remained in the other evaluations, even if it did not reach a statistical significance. Lastly, patients who responded to the treatment showed a progressive sleep efficiency reduction. The present real-life study hypothesized the influence of erenumab on chronotype, representing a link between circadian rhythm, CGRP, and migraine.

Background: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is an adult-onset multisystemic disease, affecting the peripheral nerves, heart, gastrointestinal tract, eyes, and kidneys. Nowadays, several treatment options are available; thus, avoiding misdiagnosis is crucial to starting therapy in early disease stages. However, clinical diagnosis may be difficult, as the disease may present with unspecific symptoms and signs. We hypothesize that the diagnostic process may benefit from the use of machine learning (ML). Methods: 397 patients referring to neuromuscular clinics in 4 centers from the south of Italy with neuropathy and at least 1 more red flag, as well as undergoing genetic testing for ATTRv, were considered. Then, only probands were considered for analysis. Hence, a cohort of 184 patients, 93 with positive and 91 (age- and sex-matched) with negative genetics, was considered for the classification task. The XGBoost (XGB) algorithm was trained to classify positive and negative TTR mutation patients. The SHAP method was used as an explainable artificial intelligence algorithm to interpret the model findings. Results: diabetes, gender, unexplained weight loss, cardiomyopathy, bilateral carpal tunnel syndrome (CTS), ocular symptoms, autonomic symptoms, ataxia, renal dysfunction, lumbar canal stenosis, and history of autoimmunity were used for the model training. The XGB model showed an accuracy of 0.707 ± 0.101, a sensitivity of 0.712 ± 0.147, a specificity of 0.704 ± 0.150, and an AUC-ROC of 0.752 ± 0.107. Using the SHAP explanation, it was confirmed that unexplained weight loss, gastrointestinal symptoms, and cardiomyopathy showed a significant association with the genetic diagnosis of ATTRv, while bilateral CTS, diabetes, autoimmunity, and ocular and renal involvement were associated with a negative genetic test. Conclusions: Our data show that ML might potentially be a useful instrument to identify patients with neuropathy that should undergo genetic testing for ATTRv. Unexplained weight loss and cardiomyopathy are relevant red flags in ATTRv in the south of Italy. Further studies are needed to confirm these findings.

Background and aims. Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is caused by mutations in the TTR gene, leading to misfolded monomers that aggregate generating amyloid fibrils. The clinical phenotype is heterogeneous, characterized by a multisystemic disease affecting the sensorimotor, autonomic functions along with other organs. Patisiran is a small interfering RNA acting as a TTR silencer approved for the treatment of ATTRv. Punctual and detailed instrumental biomarkers are on demand for ATTRv to measure the severity of the disease and monitor progression and response to treatment. Methods. Fifteen patients affected by ATTRv amyloidosis (66.4 ± 7.8 years, six males) were evaluated before the start of therapy with patisiran and after 9-months of follow-up. The clinical and instrumental evaluation included body weight and height; Coutinho stage; Neuropathy Impairment Score (NIS); Karnofsky performance status (KPS); Norfolk QOL Questionnaire; Six-minute walking test (6 MWT); nerve conduction studies; handgrip strength (HGS); and bioimpedance analysis (BIA). Results. Body composition significantly changed following the 9-months pharmacological treatment. In particular, the patients exhibited an increase in fat free mass, body cell mass, and body weight with a decrease in fat mass. A significant increase after 9 months of treatment was observed for the 6 MWT. Coutinho stage, KPS, NIS, NIS-W, nerve conduction studies, Norfolk, COMPASS-31 scale, and HGS remained unchanged. Conclusions. BIA might represent a useful tool to assess the effects of multiorgan damage in ATTRv and to monitor disease progression and response to treatments. More evidence is still needed for HGS. Patisiran stabilizes polyneuropathy and preserves motor strength by increasing muscle mass after 9 months of treatment.

The cluster headache is a primary headache characterized by attacks of unilateral pain associated with ipsilateral cranial autonomic features. These attacks recur in clusters during the years alternating with periods of complete remission, and their onset is often during the night. This annual and nocturnal periodicity hides a strong and mysterious link among CH, sleep, chronobiology and circadian rhythm. Behind this relationship, there may be the influence of genetic components or of anatomical structures such as the hypothalamus, which are both involved in regulating the biological clock and contributing even to the periodicity of cluster headaches. The bidirectional relationship manifests itself also with the presence of sleep disturbances in patients affected by cluster headaches. What if the key to studying the physiopathology of such disease could rely on the mechanisms of chronobiology? The purpose of this review is to analyze this link in order to interpret the pathophysiology of cluster headaches and the possible therapeutic implications.

Migraine is a common neurological disorder, affecting approximately 15% of the European population and is among the main causes of years lived with disability. In the context of increasing digitalisation, telecoaching (TC) is a new training modality that involves the use of digital tools to access and manage training services remotely. Given the well-documented benefits of physical activity in migraine management and the rapid expansion of digital health services following the COVID-19 pandemic, this scoping review aims to evaluate the use and feasibility of TC-based training programs in individuals with migraine. A systematic search was conducted on multiple databases (PubMed, Web of Science, and Scopus) identifying 1507 studies, of which only 3 met the inclusion criteria. These studies collectively involved 181 participants with migraine and assessed various training programs, including aerobic training, resistance training, and physical therapy. Most training programs showed statistically significant improvements in several variables, including severity, duration, and frequency of migraine attacks. However, based on our study, there is limited evidence to suggest that TC training is beneficial for migraine patients. These findings underscore the need for further investigation, with more rigorous methodologies, higher-quality trials, and larger sample sizes to better establish the efficacy of TC training as a preventive and therapeutic approach for migraine.