Predicting response to tVNS in patients with migraine using functional MRI: A voxels-based machine learning analysis

Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine

BACKGROUND

Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment.

METHODS

Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine.

RESULTS

One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders.

CONCLUSIONS

Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.

Clinical application of transcutaneous auricular vagus nerve stimulation: a scoping review

PURPOSE

Transcutaneous auricular vagus nerve stimulation (taVNS) is an emerging non-invasive neuromodulation therapy. This study aimed to explore the therapeutic use of taVNS, optimal stimulation parameters, effective sham protocols, and safety.

METHODS

A scoping review was conducted. Five databases and grey literature were searched. The data extracted included stimulation parameters, adverse events (AEs), and therapeutic effects on clinical outcomes.

RESULTS

109 studies were included. taVNS was used across 21 different clinical populations, most commonly in psychiatric, cardiac, and neurological disorders. Overall, 2,214 adults received active taVNS and 1,017 received sham taVNS. Reporting of stimulation parameters was limited and inconsistent. taVNS appeared to have a favourable therapeutic effect across a wide range of clinical populations with varied parameters. Three sham protocols were reported but their effectiveness was documented in only two of the 54 sham-controlled studies. Most reported adverse events were localised to stimulation site.

CONCLUSION

There is growing evidence for taVNS therapeutic effect. taVNS appears safe and tolerable. Sham protocols need evaluation. Standardised and comprehensive reporting of both stimulation parameters and adverse events is required. Two different questionnaires have been proposed to evaluate adverse events and the effectiveness of sham methods in blinding participants.

The Clinical Relevance of Artificial Intelligence in Migraine

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.

Vagal nerve stimulation for headache

PURPOSE OF REVIEW

Vagal nerve stimulation (VNS) is emerging as a probable therapeutic option for headache. Several questions remain on the mechanism of action, device parameters, efficacy, duration of treatment and long-term safety. This review aims at describing findings of recent literature pertaining to VNS for headache, to put these findings into the perspective of our current fund of knowledge and future scope of work on the topic.

RECENT FINDINGS

Several studies on functional magnetic resonance imaging patterns following VNS in patients with headache have been published, revealing potential biomarkers of headache and possible treatment effects. A study on post-COVID-19 headache and its management with noninvasive cervical VNS adds to the body of original studies. Meta-analyses and systematic reviews outlining previous work have been published. The collection of these studies adds some data to mechanisms of VNS without adding much insight to differential effects of sub-types of VNS and possible device settings that could prove to be beneficial for headache management.

SUMMARY

Well designed studies are needed to identify mechanisms of action, elicit differential effects of various stimulation parameters, and to demonstrate long-term therapeutic effects and safety of VNS in headache.

Predicting the efficacy of non-steroidal anti-inflammatory drugs in migraine using deep learning and three-dimensional T1-weighted images

Deep learning (DL) models based on individual images could contribute to tailored therapies and personalized treatment strategies. We aimed to construct a DL model using individual 3D structural images for predicting the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine. A 3D convolutional neural network model was constructed, with ResNet18 as the classification backbone, to link structural images to predict the efficacy of NSAIDs. In total, 111 patients were included and allocated to the training and testing sets in a 4:1 ratio. The prediction accuracies of the ResNet34, ResNet50, ResNeXt50, DenseNet121, and 3D ResNet18 models were 0.65, 0.74, 0.65, 0.70, and 0.78, respectively. This model, based on individual 3D structural images, demonstrated better predictive performance in comparison to conventional models. Our study highlights the feasibility of the DL algorithm based on brain structural images and suggests that it can be applied to predict the efficacy of NSAIDs in migraine treatment.

New management strategies for primary headache disorders: Insights from P4 medicine

Primary headache disorder is the main cause of headache attacks, leading to significant disability and impaired quality of life. This disorder is increasingly recognized as a heterogeneous condition with a complex network of genetic, environmental, and lifestyle factors. However, the timely diagnosis and effective treatment of these headaches remain challenging. Precision medicine is a potential strategy based on P4 (predictive, preventive, personalized, and participatory) medicine that may bring new insights for headache care. Recent machine learning advances and widely available molecular biology and imaging data have increased the usefulness of this medical strategy. Precision medicine emphasizes classifying headaches according to their risk factors, clinical presentation, and therapy responsiveness to provide individualized headache management. Furthermore, early preventive strategies, mainly utilizing predictive tools, are critical in reducing headache attacks and improving the quality of life of individuals with headaches. The current review comprehensively discusses the potential application value of P4 medicine in headache management.

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.

Imaging the brain and vascular reactions to headache treatments: a systematic review

BACKGROUND

Neuroimaging studies have made an important contribution to our understanding of headache pathophysiology. This systematic review aims to provide a comprehensive overview and critical appraisal of mechanisms of actions of headache treatments and potential biomarkers of treatment response disclosed by imaging studies.

MAIN BODY

We performed a systematic literature search on PubMed and Embase databases for imaging studies investigating central and vascular effects of pharmacological and non-pharmacological treatments used to abort and prevent headache attacks. Sixty-three studies were included in the final qualitative analysis. Of these, 54 investigated migraine patients, 4 cluster headache patients and 5 patients with medication overuse headache. Most studies used functional magnetic resonance imaging (MRI) (n = 33) or molecular imaging (n = 14). Eleven studies employed structural MRI and a few used arterial spin labeling (n = 3), magnetic resonance spectroscopy (n = 3) or magnetic resonance angiography (n = 2). Different imaging modalities were combined in eight studies. Despite of the variety of imaging approaches and results, some findings were consistent. This systematic review suggests that triptans may cross the blood-brain barrier to some extent, though perhaps not sufficiently to alter the intracranial cerebral blood flow. Acupuncture in migraine, neuromodulation in migraine and cluster headache patients, and medication withdrawal in patients with medication overuse headache could promote headache improvement by reverting headache-affected pain processing brain areas. Yet, there is currently no clear evidence for where each treatment acts, and no firm imaging predictors of efficacy. This is mainly due to a scarcity of studies and heterogeneous treatment schemes, study designs, subjects, and imaging techniques. In addition, most studies used small sample sizes and inadequate statistical approaches, which precludes generalizable conclusions.

CONCLUSION

Several aspects of headache treatments remain to be elucidated using imaging approaches, such as how pharmacological preventive therapies work, whether treatment-related brain changes may influence therapy effectiveness, and imaging biomarkers of clinical response. In the future, well-designed studies with homogeneous study populations, adequate sample sizes and statistical approaches are needed.

Application of EEG in migraine

Migraine is a common disease of the nervous system that seriously affects the quality of life of patients and constitutes a growing global health crisis. However, many limitations and challenges exist in migraine research, including the unclear etiology and the lack of specific biomarkers for diagnosis and treatment. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity. With the updating of data processing and analysis methods in recent years, EEG offers the possibility to explore altered brain functional patterns and brain network characteristics of migraines in depth. In this paper, we provide an overview of the methodology that can be applied to EEG data processing and analysis and a narrative review of EEG-based migraine-related research. To better understand the neural changes of migraine or to provide a new idea for the clinical diagnosis and treatment of migraine in the future, we discussed the study of EEG and evoked potential in migraine, compared the relevant research methods, and put forwards suggestions for future migraine EEG studies.