Migraine Detection in Young Group Based on Functional Near-Infrared Spectroscopy Measurements

This study investigated the neurovascular responses in young individuals with fewer complications using functional near-infrared spectroscopy (fNIRS). Thirty-two young migraines and thirty-two healthy control subjects (HC) were measured by fNIRS to observe changes in hemoglobin in the prefrontal cortex (PFC). According to the structural changes in the frontal cortex in migraine patients, two mental stress tasks and a concentration task (CT) were designed. The statistical findings showed that all three tasks revealed differences in prefrontal blood oxygenation between groups. Specifically, during the mental task-related exercises, a significant difference was identified in the left hemisphere, whereas during the CT, a notable distinction was noted in the right hemisphere. Furthermore, machine learning techniques were applied for migraine classification, receiving test accuracies of 82%, 89%, and 90% for the mental arithmetic task (MAT), the verbal fluency task (VFT), and the CT, respectively. These results demonstrate the feasibility of utilizing fNIRS with machine learning to classify migraines in young individuals.