Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data

Katarina Mitrović; Igor Petrušić; Aleksandra Radojičić; Marko Daković; Andrej Savić

doi:10.3389/fneur.2023.1106612

Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data

Front Neurol. 2023 Jun 23:14:1106612. doi: 10.3389/fneur.2023.1106612. eCollection 2023.

Authors

Katarina Mitrović¹, Igor Petrušić², Aleksandra Radojičić^{3

4}, Marko Daković², Andrej Savić⁵

Affiliations

¹ Department of Information Technologies, Faculty of Technical Sciences in Čačak, University of Kragujevac, Čačak, Serbia.
² Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, Belgrade, Serbia.
³ Headache Center, Neurology Clinic, Clinical Center of Serbia, Belgrade, Serbia.
⁴ Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
⁵ Science and Research Centre, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.

Abstract

Introduction: Migraine with aura (MwA) is a neurological condition manifested in moderate to severe headaches associated with transient visual and somatosensory symptoms, as well as higher cortical dysfunctions. Considering that about 5% of the world's population suffers from this condition and manifestation could be abundant and characterized by various symptoms, it is of great importance to focus on finding new and advanced techniques for the detection of different phenotypes, which in turn, can allow better diagnosis, classification, and biomarker validation, resulting in tailored treatments of MwA patients.

Methods: This research aimed to test different machine learning techniques to distinguish healthy people from those suffering from MwA, as well as people with simple MwA and those experiencing complex MwA. Magnetic resonance imaging (MRI) post-processed data (cortical thickness, cortical surface area, cortical volume, cortical mean Gaussian curvature, and cortical folding index) was collected from 78 subjects [46 MwA patients (22 simple MwA and 24 complex MwA) and 32 healthy controls] with 340 different features used for the algorithm training.

Results: The results show that an algorithm based on post-processed MRI data yields a high classification accuracy (97%) of MwA patients and precise distinction between simple MwA and complex MwA with an accuracy of 98%. Additionally, the sets of features relevant to the classification were identified. The feature importance ranking indicates the thickness of the left temporal pole, right lingual gyrus, and left pars opercularis as the most prominent markers for MwA classification, while the thickness of left pericalcarine gyrus and left pars opercularis are proposed as the two most important features for the simple and complex MwA classification.

Discussion: This method shows significant potential in the validation of MwA diagnosis and subtype classification, which can tackle and challenge the current treatments of MwA.

Keywords: artificial intelligence; classification; machine learning; magnetic resonance imaging; migraine with aura.