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BackgroundMonoclonal antibodies (mABs) targeting the calcitonin gene-related peptide (CGRP) pathway represent the first disease-specific preventive migraine therapy. Growing evidence suggests that they are effective in the preventive treatment of difficult-to-treat patients. In this study, we evaluated the psychological predictors of the outcome of treatment with the anti-CGRP monoclonal antibody erenumab in patients with chronic migraine (CM).MethodsSeventy-five patients with CM who had already failed at least 3 preventive therapies received erenumab every 28 days for a period of 12 months. Before the first administration, patients received a full psychological evaluation using The Structured Clinical Interview for DSM-5 Clinician Version (SCID-5-CV) to assess personality disturbances (primary outcome), mood and anxiety disorders, and as well specific questionnaires to evaluate alexithymia traits, childhood traumas, and current stressors (secondary outcomes).ResultsAfter 12 months of treatment, 53 patients reported a reduction of at least 50% in headache days/per month (Responders), whereas 22 did not (Non Responders). When compared to Responders, Non Responders were characterized by a higher prevalence of personality disorders belonging to Cluster C (avoidant, dependent, and obsessive-compulsive) (77% vs 37%, p = .001). Non Responders were also characterized by a higher prevalence of anxiety disorders (90% vs 60%, p = 0.007), showed more alexithymic traits (51.7 ± 13.7 vs 42.9 ± 14.3, p = 0.017), and reported a higher number of 'at least serious' current stressors (3.2 ± 4.0 vs 0.8 ± 1.4, p < .0001) than Responders. At the multivariate analysis, higher prevalence of Cluster C personality disorders (OR 3.697; p = 0.05) and higher number of ‘at least serious’ life events (OR 1.382; p = 0.017) arose as prognostic factors of erenumab failure.ConclusionsErenumab confirmed its effectiveness in a population of difficult-to-treat migraine. The presence of “anxious-fearful” personality together with current stressors and anxiety represent negative predictors of treatment outcome.Trial registrationThe study protocol was registered at clinicaltrials.gov (NCT04361721).

Background Cluster headache (CH) is a rare primary headache disorder characterized by recurrent episodes of strictly unilateral excruciating pain accompanied by trigemino-autonomic signs, which significantly impacts the quality of life, social interactions, and occupational functioning of those who are affected. To promote a better understanding of this disabling condition and to foster research on the topic, this review provides a comprehensive description of the hallmarks of CH, including its clinical presentation, diagnostic challenges, pathophysiology, and current and novel therapeutic targets. It concludes by describing the disease burden and advocating for significant improvements in healthcare systems, and promoting health equity, as well as reducing stigma. Principal findings Despite its distinctive clinical and chronobiological features, CH may be mistaken for other primary headache disorders or different types of orofacial pain. Key pathogenic characteristics include the activation of the trigeminal-autonomic system with the release of several neuropeptides, the involvement of the hypothalamus in regulating the circadian rhythm, genetic variants, and the mesolimbic system. Both invasive and non-invasive neuromodulation treatments have been used to target the trigemino-cervical, parasympathetic, and hypothalamic systems. Additionally, novel therapeutic targets are currently being study. Alongside canonical therapies, several complementary approaches have been explored over the years, with most evidence deriving from uncontrolled research involving individuals who do not respond to standard pharmacological treatments. Despite advancements in our understanding of this complex disease, CH continues to pose considerable social, economic, and psychological challenges. Advocacy is essential and should prioritize early diagnosis, alleviate stigma, provide specialized training for healthcare professionals, and offer support to and through patient associations. Conclusions CH is characterised by a complex, multifactorial, pathophysiology that is still not fully understood. Precise diagnosis, additional research studies, and robust psychosocial and institutional support are necessary to improve the quality of life for individuals affected by this debilitating condition.

Objective This study aims to evaluate the feasibility of an integrated multi-domain telerehabilitation (TR) system in stroke patients and to observe whether there are changes in the quality of life (QoL) levels of patients undergoing TR treatments. Methods Patients were enrolled for a longitudinal multicentric pilot study conducted in six Italian research hospitals (IRCCS). The primary outcome was the feasibility of an integrated TR system, assessed by calculating treatment adherence and by collecting data from the Technology Acceptance Model and the System Usability Scale (SUS). Information on time and travel distance savings was also collected. As secondary outcomes, we evaluated changes in QoL levels with the EuroQol 5-dimensions (EQ-5D) and the Short Form-36 (SF-36) and in caregiver burden through the Zarit Burden Inventory. Results We enrolled 84 patients. Our system turned out to be feasible (treatment adherence = 85%), usable (SUS = 73.36/100, classifying it as a ‘good’ system) and well accepted by patients. Quality of life levels improved significantly from pre- to post-treatment (EQ-5D: p = 0.0014; SF-36 general health: p = 0.047). Caregivers perceived little or no significant care burden. Conclusions Telerehabilitation has been confirmed to be a feasible, usable and acceptable solution to guarantee continuity of care and improve accessibility to rehabilitation treatments to post-stroke patients. Furthermore, the strength of TR is in the possibility to improve patients’ QoL, which in turn could impact on functioning.

The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson’s disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.

The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.

In the past few years the scientific community has witnessed a prodigious surge in research activity, publication of data and progress in understanding the mechanistic components of migraine. This renaissance is the result of efforts initiated decades ago that are finally being translated into benefits for individuals affected by this disease.Key advancesTrials have demonstrated that monoclonal antibodies that target calcitonin gene-related peptide 1 induce marked improvements (>75%) among a small but meaningful proportion of patients with chronic migraine1 or episodic migraine2.Noninvasive vagal nerve stimulation delivered with two short-duration stimulations at the neck level has proved effective in the treatment of migraine attacks: one-third of patients achieved pain-free status at 2 h (ref.4).Preclinical data support the idea that pituitary adenylate cyclase-activating polypeptide and its G-protein-coupled receptors are viable targets for new migraine treatments8.Intriguing findings obtained in a migraine-specific animal model point to another potential pathway implicated in migraine pain: inhibition of acid-sensing ion channels prevents cephalic allodynia10.

Migraine is a major neurological disorder affecting one in nine adults worldwide with a significant impact on health care and socioeconomic systems. Migraine is more prevalent in women than in men, with 17% of all women meeting the diagnostic criteria for migraine. In women, the frequency of migraine attacks shows variations over the menstrual cycle and pregnancy, and the use of combined hormonal contraception (CHC) or hormone replacement therapy (HRT) can unveil or modify migraine disease. In the general population, 18–25% of female migraineurs display a menstrual association of their headache. Here we present an overview on the evidence supporting the role of reproductive hormones, in particular estrogens, in the pathophysiology of migraine. We also analyze the efficacy and safety of prescribing exogenous estrogens as a potential treatment for menstrual-related migraine. Finally, we point to controversial issues and future research areas in the field of reproductive hormones and migraine.

ObjectiveTo critically appraise the evidence for and against premonitory symptoms in migraine being due to hypothalamic dysfunction.DiscussionSome premonitory symptoms (e.g. fatigue, mood changes, yawning, and food craving) are associated with the physiologic effects of neurotransmitters such as orexins, neuropeptide Y, and dopamine; all of which are expressed in hypothalamic neurons. In rodents, electrophysiologic recordings have shown that these neurotransmitters modulate nociceptive transmission at the level of second-order neurons in the trigeminocervical complex (TCC). Additional insights have been gained from neuroimaging studies that report hypothalamic activation during the premonitory phase of migraine. However, the available evidence is limited by methodologic issues, inconsistent reporting, and a lack of adherence to ICHD definitions of premonitory symptoms (or prodromes) in human experimental studies.ConclusionsThe current trend to accept that premonitory symptoms are due to hypothalamic dysfunction might be premature. More rigorously designed studies are needed to ascertain whether the neurobiologic basis of premonitory symptoms is due to hypothalamic dysfunction or rather reflects modulatory input to the trigeminovascular system from several cortical and subcortical areas. On a final note, the available epidemiologic data raises questions as to whether the existence of premonitory symptoms and even more so a distinct premonitory phase is a true migraine phenomenon.Video recording of the debate held at the 1st International Conference on Advances in Migraine Sciences (ICAMS 2022, Copenhagen, Denmark) is available at: https://www.youtube.com/watch?v=d4Y2x0Hr4Q8.

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson’s disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1–6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.