Quantifying Structural Changes in Cultured Neurons Using Fractal Analysis

Quantitative analysis of neuronal morphology in dense cultures remains technically challenging, as extensive overlap between neuritic arbors and background signal limits the applicability of traditional tracing-based approaches. Here, we applied fractal dimension (FD) and lacunarity (LAC) analysis as scale-invariant, global measures of neurite complexity and systematically compared them with classical morphometric approaches in dense primary hippocampal neuronal cultures. Neurons were treated with the Abl tyrosine kinase inhibitor imatinib to induce cytoskeletal remodeling. Fractal analysis was performed on skeletonized representations derived from standardized binarized confocal images, enabling robust quantification of neurite space-filling properties across spatial scales. Imatinib treatment induced a pronounced reduction in FD (DMSO: ~1.56 ± 0.04 vs. IMA: ~1.36 ± 0.05, p < 0.001) together with increased lacunarity, indicating reduced multiscale complexity and increased spatial heterogeneity. In contrast, classical morphometric analysis revealed a heterogeneous phenotype, with elongation of primary neurites accompanied by reduced branching. These opposing effects partially compensated for one another, obscuring overall complexity changes when assessed using individual parameters. To integrate multivariate features, we constructed a composite Neurite Complexity Index (NCI) incorporating branching, neurite length, and distribution-sensitive descriptors (R² ≈ 0.98; F(4,106) = 89.6, p < 1 × 10⁻¹⁸⁴). FD showed strong convergence with the NCI across bootstrap analyses, and clustering and discriminant analyses confirmed its high discriminatory power. Together, these findings suggest that fractal analysis may provide a sensitive and compact framework within the present experimental context for detecting treatment-associated structural changes in dense neuronal cultures.