Diagnostic Testing for Migraine and Other Primary Headaches

Neuropsychological Function in Migraine Headaches: An Expanded Comprehensive Multidomain Meta-Analysis

BACKGROUND AND OBJECTIVES

A sizable literature has studied neuropsychologic function in persons with migraine (PwM), but despite this, few quantitative syntheses exist. These focused on circumscribed areas of the literature. In this study, we conducted an expanded comprehensive meta-analysis comparing performance on clinical measures of neuropsychological function both within and across domains, between samples of PwM and healthy controls (HCs).

METHODS

For this Meta-analyses Of Observational Studies in Epidemiology-compliant meta-analysis, a unified search strategy was applied to OneSearch (a comprehensive collection of electronic databases) to identify peer-reviewed original research published across all years up until August 1, 2023. Using random-effects modeling, we examined aggregated effect sizes (Hedges' g), between-study heterogeneity (Cochran Q and I2), moderating variables (meta-regression and subgroup analyses), and publication bias (Egger regression intercept and Duval and Tweedie Trim-and-Fill procedure). Study bias was also coded using the NIH Study Quality Assessment Tools.

RESULTS

Omnibus meta-analysis from the 58 studies included (PwM n = 5,452, HC n = 16,647; 612 effect sizes extracted) indicated lower overall cognitive performance in PwM vs HCs (g = -0.37; 95% CI -0.47 to -0.28; p < 0.001), and high between-study heterogeneity (Q = 311.25, I2 = 81.69). Significant domain-specific negative effects were observed in global cognition (g = -0.46, p < 0.001), executive function (g = -0.45, p < 0.001), processing speed (g = -0.42, p < 0.001), visuospatial/construction (g = -0.39, p = 0.006), simple/complex attention (g = -0.38, p < 0.001), learning/memory (g = -0.25, p < 0.001), and language (g = -0.24, p < 0.001). Orientation (p = 0.146), motor (p = 0.102), and intelligence (p = 0.899) were not significant. Moderator analyses indicated that age (particularly younger HCs), samples drawn from health care facility settings (e.g., tertiary headache centers) vs community-based populations, and higher attack duration were associated with larger (negative) effects and accounted for a significant proportion of between-study heterogeneity in effects. Notably, PwM without aura yielded stronger (negative) effects (omnibus g = -0.37) vs those with aura (omnibus g = -0.10), though aura status did not account for heterogeneity observed between studies.

DISCUSSION

Relative to HCs, PwM demonstrate worse neurocognition, as detected by neuropsychological tests, especially on cognitive screeners and tests within executive functioning and processing speed domains. Effects were generally small to moderate in magnitude and evident only in clinic (vs community) samples. Aura was not meaningfully associated with neurocognitive impairment.

Hemiplegic Migraine in Children and Adolescents

BACKGROUND

Only a few studies have focused on hemiplegic migraine (HM) in children despite its early age of onset. The aim of this review is to describe the peculiar characteristics of pediatric HM.

METHODS

This is a narrative review based on 14 studies on pediatric HM selected from 262 papers.

RESULTS

Different from HM in adults, pediatric HM affects both genders equally. Early transient neurological symptoms (prolonged aphasia during a febrile episode, isolated seizures, transient hemiparesis, and prolonged clumsiness after minor head trauma) can precede HM long before its onset. The prevalence of non-motor auras among children is lower than it is in adults. Pediatric sporadic HM patients have longer and more severe attacks compared to familial cases, especially during the initial years after disease onset, while familial HM cases tend to have the disease for longer. During follow-up, the frequency, intensity, and duration of HM attacks often decrease. The outcome is favorable in most patients; however, neurological conditions and comorbidities can be associated.

CONCLUSION

Further studies are needed to better define the clinical phenotype and the natural history of pediatric HM and to refine genotype-phenotype correlations in order to improve the knowledge on HM physiopathology, diagnosis, and outcome.

Global Epidemiology, Clinical Features, Diagnosis and Current Therapeutic Novelties in Migraine Therapy and their Prevention: A Narrative Review

INTRODUCTION

The current article reviews the latest information on epidemiology, clinical features, diagnosis, recent advancements in clinical management, current therapeutic novelties, and the prevention of migraines. In a narrative review, all studies as per developed MeSH terms published until February 2023, excluding those irrelevant, were identified through a PubMed literature search.

METHODS

Overall, migraine affects more than a billion people annually and is one of the most common neurological illnesses. A wide range of comorbidities is associated with migraines, including stress and sleep disturbances. To lower the worldwide burden of migraine, comprehensive efforts are required to develop and enhance migraine treatment, which is supported by informed healthcare policy. Numerous migraine therapies have been successful, but not all patients benefit from them.

RESULTS

CGRP pathway-targeted therapy demonstrates the importance of translating mechanistic understanding into effective treatment. In this review, we discuss clinical features, diagnosis, and recently approved drugs, as well as a number of potential therapeutic targets, including pituitary adenylate cyclase-activating polypeptide (PACAP), adenosine, opioid receptors, potassium channels, transient receptor potential ion channels (TRP), and acid-sensing ion channels (ASIC).

CONCLUSION

In addition to providing more treatment options for improved clinical care, a better understanding of these mechanisms facilitates the discovery of novel therapeutic targets.

A Comprehensive Review of Zavegepant as Abortive Treatment for Migraine

Migraine headache is a widespread and complex neurobiological disorder that is characterized by unilateral headaches that are often accompanied by photophobia and phonophobia. Migraine is one of the leading chief complaints in the emergency department with negative impacts on quality of life and activities of daily living. The high number of emergency presentations also results in a significant economic burden. Its risk factors include family history, genetics, sex, race, socioeconomics, the existence of comorbid conditions, and level of education. Triggers include stress, light, noise, menstruation, weather, changes in sleep pattern, hunger, dehydration, dietary factors, odors, and alcohol. The International Headache Society has defined criteria for the diagnosis of migraine with and without aura. The pathophysiology of migraine headaches is multifactorial so there are a variety of treatment approaches. The current treatment approach includes abortive medications and prophylactic medications. Abortive medications include the first-line treatment of triptans, followed by ergot alkaloids, and calcitonin gene-related peptide (CGRP) receptor antagonists along with supplemental caffeine and antiemetics. Trigeminal afferents from the trigeminal ganglion innervate most cranial tissues and many areas of the head and face. These trigeminal afferents express certain biomarkers such as calcitonin gene-related peptide (CGRP), substance P, neurokinin A, and pituitary adenylate cyclase-activating polypeptide that are important to the pain and sensory aspect of migraines. In this comprehensive review, we discuss Zavegepant, a calcitonin gene-related peptide receptor antagonist, as a new abortive medication for migraine headaches.

Pathophysiological Bases of Comorbidity in Migraine

Despite that it is commonly accepted that migraine is a disorder of the nervous system with a prominent genetic basis, it is comorbid with a plethora of medical conditions. Several studies have found bidirectional comorbidity between migraine and different disorders including neurological, psychiatric, cardio- and cerebrovascular, gastrointestinal, metaboloendocrine, and immunological conditions. Each of these has its own genetic load and shares some common characteristics with migraine. The bidirectional mechanisms that are likely to underlie this extensive comorbidity between migraine and other diseases are manifold. Comorbid pathologies can induce and promote thalamocortical network dysexcitability, multi-organ transient or persistent pro-inflammatory state, and disproportionate energetic needs in a variable combination, which in turn may be causative mechanisms of the activation of an ample defensive system with includes the trigeminovascular system in conjunction with the neuroendocrine hypothalamic system. This strategy is designed to maintain brain homeostasis by regulating homeostatic needs, such as normal subcortico-cortical excitability, energy balance, osmoregulation, and emotional response. In this light, the treatment of migraine should always involves a multidisciplinary approach, aimed at identifying and, if necessary, eliminating possible risk and comorbidity factors.

Blood Biomarkers for Assessing Headaches in Healthcare Workers after Wearing Biological Personal Protective Equipment in a COVID-19 Field Hospital

The coronavirus disease 2019 (COVID-19) has led to a pandemic, which among other things, has highlighted biosafety as a key cornerstone in the management of disease transmission. The aim of this work was to analyze the role played by different blood biomarkers in predicting the appearance of headaches in healthcare workers wearing personal protective equipment (PPE) in a COVID-19 treatment unit. A prospective cohort study of 38 healthcare workers was performed during April 2020. Blood analysis, performed just before the start of a 4 hour shift, was carried out on all volunteers equipped with PPE. At the end of their shifts and after decontamination, they were asked if they had suffered from headache in order to obtain a binary outcome. The baseline creatinine value reflected a specific odds ratio of 241.36 (95% CI: 2.50-23,295.43; p = 0.019) and an area under the curve (AUC) value of 0.737 (95%CI: 0.57-0.90; p < 0.01). Blood creatinine is a good candidate for predicting the appearance of a de novo headache in healthcare workers after wearing PPE for four hours in a COVID-19 unit.

Blood Biomarkers for Assessing Headaches in Healthcare Workers after Wearing Biological Personal Protective Equipment in a COVID-19 Field Hospital

The coronavirus disease 2019 (COVID-19) has led to a pandemic, which among other things, has highlighted biosafety as a key cornerstone in the management of disease transmission. The aim of this work was to analyze the role played by different blood biomarkers in predicting the appearance of headaches in healthcare workers wearing personal protective equipment (PPE) in a COVID-19 treatment unit. A prospective cohort study of 38 healthcare workers was performed during April 2020. Blood analysis, performed just before the start of a 4 hour shift, was carried out on all volunteers equipped with PPE. At the end of their shifts and after decontamination, they were asked if they had suffered from headache in order to obtain a binary outcome. The baseline creatinine value reflected a specific odds ratio of 241.36 (95% CI: 2.50-23,295.43; p = 0.019) and an area under the curve (AUC) value of 0.737 (95%CI: 0.57-0.90; p < 0.01). Blood creatinine is a good candidate for predicting the appearance of a de novo headache in healthcare workers after wearing PPE for four hours in a COVID-19 unit.

An Evidence-Based Review of Galcanezumab for the Treatment of Migraine

PURPOSE OF REVIEW

This is a comprehensive review of the current literature on the usage of galcanezumab for migraine treatment. It reviews the biology, pathophysiology, epidemiology, diagnosis, and conventional treatment of migraines, then compares the literature available for galcanezumab with historical treatment options.

RECENT FINDINGS

Migraine is a common headache disorder and constitutes a significant source of distress from both a personal and societal perspective. Conventional treatment includes abortive and preventive treatment. Treatment options are limited and may be only partially or minimally effective in some of the population. Recent evidence points to metabolic changes in the brain as possible causes of migraine, via reduced available energy or a spiking need for it, resulting in a relative insufficiency. This leads to trigeminocervical complex (TCC) activation and a headache episode, modulated by calcitonin gene-related peptide (CGRP). Galcanezumab (Emgality) is a monoclonal antibody targeting CGRP that is given in a monthly injection for the prevention of migraines. Its safety was previously shown in phase 1 and 2 trials, and recent phase 3 trials showed efficacy, with up to 50% reduction in monthly migraine days and improved functional capacity in migraineurs. Studies show that the drug is well tolerated and safe. Migraine headache is a common neurological syndrome that causes great pain and suffering. Treatment options today are limited. Galcanezumab does not prevent migraines, but it is effective in decreasing their frequency and length. It is also much better tolerated than the currently existing therapies. While it is unlikely to provide monotherapy for migraines, it is a novel therapy that may be added for cases of severe or frequent migraines.

EEG Characteristics and Diagnostic Implications in Childhood Headache: A Multi-Center Study

Introduction: Epilepsy and migraines are frequently observed as comorbidities, with the occurrence of one disorder increasing the probability of the other. The aim of our study was to evaluate the EEG characteristics by the type of headache and the implications of EEGs in headache patients, comparing the clinical characteristics and treatments between the headache patients with normal and abnormal EEGs. Methods: We conducted a retrospective analysis reviewing the medical records of 259 patients with headaches who visited the pediatrics departments of five university hospitals and underwent EEGs over a period of 3 years. Based on the data entered, analyses of the following items were conducted: (1) comparison of the EEG abnormalities by the type of headache and the characteristics of the EEG findings and (2) comparison of the clinical characteristics between patients with normal and abnormal EEGs. Results: Of the 259 patients, 31 showed abnormal EEGs, while 228 had normal EEGs. Of the 31 patients with abnormal EEGs, 17 showed epileptiform discharges, and 11 showed rhythmic slowing. The frequency of EEG abnormalities was significantly high in patients with migraines with auras than other types of headache. The Pediatric Migraine Disability Assessment (PedMIDAS) score was significantly higher in the abnormal EEG group compared with the normal EEG group (p = 0.001). Conclusion: The results of this study suggest that the abnormal EEG group had more significant disruptions in their daily lives due to headaches than the normal EEG group and that patients with migraines with aura may need EEGs and they might also have overlapping pathophysiologic mechanisms with epilepsy.

Ultrasonographic measurement of the optic nerve sheath in the differential diagnosis and follow-up of migraine with and without aura: A pilot study

OBJECTIVE

Headache continues to be a common cause of emergency admissions. At first glance, primary headaches may not be distinguished from secondary headaches. This study aimed to evaluate the use of ultrasonic measurement of optic nerve sheath diameter (ONSD) in the clinical diagnosis of migraine patients during attack and during periods without attacks.

PATIENTS AND METHODS

This prospective study was conducted at the emergency department and neurology outpatient clinic of a tertiary hospital between October 2019 and January 2020. Patients diagnosed with episodic migraine and between ages 18-60 were included in the study. Migraine diagnosis and classification were based on criteria from the 2018 International Classification of Headache Disorders. ONSD measurement was performed with ultrasonography. ONSD measurements of the same migraine patients during attack and attack-free periods were compared with measurements of healthy volunteers.

RESULTS

A total of 82 participants consisting of 42 migraine patients and 40 control subjects were evaluated. In the control group, mean ONSD of the right eye was 4.88 ± 0.31 mm and mean ONSD of the left eye was 4.86 ± 0.31 mm. Mean ONSD measurement of the left eye was 4.80 ± 0.36 mm and mean ONSD of the right eye was 4.82 ± 0.37 during the attack period of the migraine patients. There was no significant difference between the migraine patients and the control group according to right and left eye ONSD measurements (attack-free period: p = 0.200, p = 0.448; attack period: p = 0.338, p = 0.660, respectively). There was no significant difference between ONSD measurements during the attack period and attack-free period in migraine patients (right eye p = 0.32; left eye p = 0.532).

CONCLUSION

The results of our study indicate that ONSD measurements may provide clinical insight into differential diagnosis preceding neuroimaging in migraine patients. On the other hand, since our study is the first on this topic, it should be noted that further studies are needed to come to a conclusion.

Automatic migraine classification using artificial neural networks

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients' health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient's symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

Automatic migraine classification using artificial neural networks

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients' health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient's symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.