ArticleFrontiers in neurology2025
T1- and diffusion tensor-based fractal dimension of white and grey matter in multiple sclerosis.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Multiple sclerosis (MS) changes brain microstructure even at early disease stages, with changes detectable in normal-appearing white matter (NAWM) and grey matter (GM). Diffusion tensor imaging (DTI) is sensitive to such alterations, while fractal dimension (FD) provides complementary information on tissue complexity. We hypothesized that T1-based FD offers additional diagnostic value beyond DTI-derived metrics of tissue integrity, including fractional anisotropy (FA) and mean diffusivity (MD). Methods: MRI data were acquired from 120 patients with relapsing-remitting MS and low mean Expanded Disability Status Scale (EDSS) scores, as well as 75 healthy control (HC) participants. FD, FA, and MD were quantified in global brain tissues and within white matter (WM) skeletons, both with and without lesion masking. The interactions between FD and DTI metrics were assessed, and classification models were constructed to evaluate diagnostic performance. Results: WM in MS exhibited reduced FD and FA alongside elevated MD, consistent with demyelination and axonal degradation. GM demonstrated higher FD, FA, and MD values, suggesting a more nuanced interplay of inflammatory remodeling, dendritic reorganization and compensatory structural adaptation. The limited impact of lesion masking on group-average metrics, but its marked effect on FD-DTI interactions, revealed that lesions regulate structural variance without dominating global tissue complexity. FD proved particularly sensitive at tissue interfaces, where geometry is disrupted, whereas WM skeleton analyses reflected preserved regularity of core tracts, even amid microstructural degeneration. Discussion: Our findings support the concept of a surface-in gradient of complexity loss in MS. Combining FD with FA and MD substantially improved classification accuracy, particularly in WM skeleton-based models, emphasizing the diagnostic potential of geometric-microstructural integration. Widespread associations of FA with clinical covariates and age further suggest that diffuse WM alterations underpin both cognitive and clinical decline.
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