Differentiating episodic migraine from healthy controls using fractal dimension analysis of MRI cortical morphology

BackgroundThe diagnosis of migraine currently relies on clinical criteria based on expert consensus. Despite advances in neuroimaging, a sensitive and reliable morphological biomarker for episodic migraine (EM) remains elusive. Fractal dimension (FD), a novel morphometric metric, offers promise for detecting minor cortical alterations and may offer more precise quantification of cerebral folding than conventional cortical thickness (CT) analysis.MethodsThis study compared 50 EM patients and 50 matched healthy controls (HC) using both FD and CT analyses of magnetic resonance imaging (MRI) across hemispheres, cerebral lobes and 68 cortical regions. After evaluating data normality, group differences were assessed using false discovery rate-corrected <i>t</i>-tests and validated through permutation testing. Unsupervised <i>k</i>-means clustering was applied to evaluate classification performance.ResultsFD showed lower variance and narrower data distribution, revealing more significant cortical alterations than CT, especially in the temporal lobe. Notably, FD uniquely identified structural changes in the insula, a region implicated in high-frequency migraine attacks, where CT showed no differences. The FD-based classifier achieved 81.62% accuracy in distinguishing EM from HC, significantly outperforming CT (54.03%).ConclusionsFD-based analysis of structural MRI shows greater sensitivity than conventional method in detecting migraine-related cortical changes, effectively distinguishing EM from HC, even in low-frequency cases with minor alterations previously undetectable by MRI. This approach may hold promise for supporting clinical diagnosis and enabling future automated screening.