RESUMO
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.
RESUMO
BACKGROUND: The mechanism of compensatory hyperhidrosis remains unclear. The aim of this study was to explore the relationship between compensatory hyperhidrosis and thoracic sympathetic ganglion excitability to assess the effectiveness of thoracoscopic T4 sympathicotomy for treating palmar hyperhidrosis. METHODS: Sixty-six cases of T4 sympathetic ganglions were prospectively collected from patients with palmar hyperhidrosis who underwent thoracoscopic T4 sympathicotomy from 2013 to 2016 in our department. The expression levels of choline acetyltransferase (ChAT), vasoactive intestinal peptide (VIP), and synaptophysin were detected using immunohistochemistry. Patients with palmar hyperhidrosis were followed-up for examination of postoperative sweating status. RESULTS: Thirty-eight cases (57.6%) of compensatory hyperhidrosis were identified. Mild compensatory hyperhidrosis occurred in 26 patients (39.4%), moderate in 11 (16.7%), and severe in 1 (1.5%). The rate of compensatory hyperhidrosis was higher in patients with axilla hyperhidrosis than those without (76.0% vs. 46.3%, P=0.018). However, the clinical data were similar between the compensatory hyperhidrosis group and the no compensatory hyperhidrosis group. In addition, the ChAT, VIP, and synaptophysin expression levels were not significantly different between the two groups (P values of 0.356, 0.071, and 0.141, respectively). Furthermore, the ChAT, VIP, and synaptophysin expression levels in the mild group were similar to those observed in the moderate/intense group (P values of 0.089, 0.124, and 0.149, respectively). The remission rate was 100% in palmar hyperhidrosis, 48.2% (27/56) in pedal hyperhidrosis, 56.0% (14/25) in axilla hyperhidrosis and 88.9% (16/18) in skin symptoms. No signs of chapped skin on the palms were found. CONCLUSIONS: There was no significant correlation between compensatory hyperhidrosis and thoracic sympathetic ganglion excitability; however, compensatory hyperhidrosis is more likely to simultaneously occur in patients with axilla hyperhidrosis. The satisfactory efficacy of thoracoscopic T4 sympathicotomy indicates that it may an ideal technique for palmar hyperhidrosis.
