Evaluation of the Impact of the COVID-19 Lockdown in the Clinical Course of Migraine

Objective: Previous studies have demonstrated that emotional stress, changes in lifestyle habits and infections can worsen the clinical course of migraine. We hypothesize that changes in habits and medical care during coronavirus disease 2019 (COVID-19) lockdown might have worsened the clinical course of migraine. Design: Retrospective survey study collecting online responses from migraine patients followed-up by neurologists at three tertiary hospitals between June and July 2020. Methods: We used a web-based survey that included demographic data, clinical variables related with any headache (frequency) and migraine (subjective worsening, frequency, and intensity), lockdown, and symptoms of post-traumatic stress. Results: The response rate of the survey was 239/324 (73.8%). The final analysis included 222 subjects. Among them, 201/222 (90.5%) were women, aged 42.5 ± 12.0 (mean±SD). Subjective improvement of migraine during lockdown was reported in 31/222 participants (14.0%), while worsening in 105/222 (47.3%) and was associated with changes in migraine triggers such as stress related to going outdoors and intake of specific foods or drinks. Intensity of attacks increased in 67/222 patients (30.2%), and it was associated with the subjective worsening, female sex, recent insomnia, and use of acute medication during a headache. An increase in monthly days with any headache was observed in 105/222 patients (47.3%) and was related to symptoms of post-traumatic stress, older age and living with five or more people. Conclusions: Approximately half the migraine patients reported worsening of their usual pain during the lockdown. Worse clinical course in migraine patients was related to changes in triggers and the emotional impact of the lockdown. © 2021 The Author(s) 2021. Published by Oxford University Press on behalf of the American Academy of Pain Medicine

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise