Abstract
Machine learning (ML) has been largely applied for predicting migraine classification. However, the prediction of efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine is still in the early stages. This study aims to evaluate whether the combination of machine learning and amygdala-related functional features could help predict the efficacy of NSAIDs in patients with migraine without aura (MwoA). A total of 70 MwoA patients were enrolled for the study, including patients with an effective response to NSAIDs (M-eNSAIDs, n = 35) and MwoA patients with ineffective response to NSAIDs (M-ieNSAIDs, n = 35). Furthermore, 33 healthy controls (HCs) were matched for age, sex, and education level. The study participants were subjected to resting-state functional magnetic resonance imaging (fMRI) scanning. Disrupted functional connectivity (FC) patterns from amygdala-based FC analysis and clinical characteristics were considered features that could promote classification through multivariable logistic regression (MLR) and support vector machine (SVM) for predicting the efficacy of NSAIDs. Further, receiver operating characteristic (ROC) curves were drawn to evaluate the predictive ability of the models. The M-eNSAIDs group exhibited enhanced FC with ipsilateral calcarine sulcus (CAL), superior parietal gyrus (SPG), paracentral lobule (PCL), and contralateral superior frontal gyrus (SFG) in the left amygdala. However, the M-eNSAIDs group showed decreased FC with ipsilateral caudate nucleus (CAU), compared to the M-ieNSAIDs group. Moreover, the M-eNSAIDs group showed higher FC with left pre-central gyrus (PreCG) and post-central gyrus (PoCG) compared to HCs. In contrast, the M-ieNSAIDs group showed lower FC with the left anterior cingulate cortex (ACC) and right SFG. Furthermore, the MwoA patients showed increased FC with the left middle frontal gyrus (MFG) in the right amygdala compared to HCs. The disrupted left amygdala-related FC patterns exhibited significant correlations with migraine characteristics in the M-ieNSAIDs group. The MLR and SVM models discriminated clinical efficacy of NSAIDs with an area under the curve (AUC) of 0.891 and 0.896, sensitivity of 0.971 and 0.833, and specificity of 0.629 and 0.875, respectively. These findings suggest that the efficacy of NSAIDs in migraine could be predicted using ML algorithm. Furthermore, this study highlights the role of amygdala-related neural function in revealing underlying migraine-related neuroimaging mechanisms.
Highlights
Migraine is the leading cause of disability among young women
To the best of our knowledge, this was the first study conducted to predict the clinical efficacy of Non-steroidal anti-inflammatory drugs (NSAIDs) in migraine without aura (MwoA) patients
The study revealed that the support vector machine (SVM) classifier demonstrated predictive ability with an accuracy of more than 89%, treating abnormal amygdala-related FC patterns and clinical characteristics as features of interest
Summary
It is the second leading cause of disability worldwide affecting over 15% of the population (GBD 2015 Disease and Injury Incidence and Prevalence Collaborators, 2016) It is a common prevalent neurological disorder characterized by recurrent moderate to severe headaches and is aggravated by physical activity. It has been shown that migraineurs who tend to overuse NSAIDs suffer from treatment-resistant headaches and cognitive decline more frequently (Cai et al, 2019). It was shown in an observational study that migraine patients with or without medication overuse had an increased risk of developing depression. There is a need to have further understanding of inter-individual variability affecting the response to NSAIDs to allow optimization of therapy
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