Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods

• Published in: Medical & biological engineering & computing Vol. 62; no. 8; pp. 2545 - 2556
• Main Authors: Fernandes, Orlando, Ramos, Lucas Rego, Acchar, Mariana Calixto, Sanchez, Tiago Arruda
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024; Springer Nature B.V
• Subjects: Activation analysis; Activity patterns; Adult; Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; brain; Brain - diagnostic imaging; Brain - physiopathology; Brain Mapping - methods; Computer Applications; Embedding; Female; Functional magnetic resonance imaging; Gaussian process; Headache; Human Physiology; Humans; Imaging; Information processing; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; magnetism; Male; Migraine; Migraine with Aura - diagnostic imaging; Migraine with Aura - physiopathology; Neuroimaging; normal distribution; Original Article; Radiology; therapeutics; Visual discrimination; Visual discrimination learning; Visual stimuli; Migraine with aura (MWA); Spatiotemporal classifier; fMRI; Machine learning
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-024-03080-5

Functional magnetic resonance imaging (fMRI) studies on migraine with aura are challenging due to the rarity of patients with triggered cases. This study optimized methodologies to explore differences in ictal and interictal spatiotemporal activation patterns based on visual stimuli using fMRI in two patients with unique aura triggers. Both patients underwent separate fMRI sessions during the ictal and interictal periods. The Gaussian Process Classifier (GPC) was used to differentiate these periods by employing a machine learning temporal embedding approach and spatiotemporal activation patterns based on visual stimuli. When restricted to visual and occipital regions, GPC had an improved performance, with accuracy rates for patients A and B of roughly 86–90% and 77–81%, respectively ( p  < 0.01). The algorithm effectively differentiated visual stimulation and rest periods and identified times when aura symptoms manifested, as evident from the varying predicted probabilities in the GPC models. These findings contribute to our understanding of the role of visual processing and brain activity patterns in migraine with aura and the significance of temporal embedding techniques in examining aura phenomena. This finding has implications for diagnostic tools and therapeutic techniques, especially for patients suffering from aura symptoms. Graphical Abstract