Microglial Form, Function, and Fractal Dimension: Responsible Approaches in the Wild West of AI

Microglia, the resident immune cells of the central nervous system, play essential roles in brain development and homeostasis. Over recent decades, increasing attention has been directed toward their highly complex morphology and dynamic behavior. Fractal analysis has emerged as a quantitative methodological framework for capturing this complexity, offering scalable and mathematically grounded descriptors of microglial form and structural dynamics across experimental conditions.

In parallel, the rapid expansion of artificial intelligence (AI) and machine learning technologies has created new opportunities for computational and image-based modeling of microglial morphology, spatial organization, and interactions. However, many current AI-driven approaches prioritize visual realism or computational convenience over biological and methodological grounding. As a result, key factors such as dynamic cellular interactions, temporal evolution, deformability, and model validation against experimental data are often underrepresented, limiting interpretability, rigor, and reproducibility.

This Research Topic focuses on innovative, rigorously validated methodological approaches for studying microglial form and behavior using fractal analysis and AI-based modeling. Contributions should emphasize method development, benchmarking of assumptions, and transparent evaluation of what constitutes “realism” in computational and experimental models, rather than descriptive applications or disease-oriented interpretations. The goal is to advance generalizable, hypothesis-driven, and reproducible frameworks that integrate quantitative imaging, experimental neuroscience, and computational modeling.

We particularly welcome submissions that:
• Apply fractal analysis as a quantitative and reproducible method to characterize microglial morphology and structural dynamics
• Compare AI-based digital or computational models with experimental microglial data to assess methodological fidelity and validation strategies
• Quantitatively investigate microglial interactions with other cellular or structural components using experimental or computational approaches
• Advance or critically evaluate methodological developments in fractal analysis relevant to neuroscience research
• Assess AI tools and algorithms for modeling microglial behavior with emphasis on transparency, interpretability, validation, and reproducibility
• Integrate experimental and computational approaches, including time-resolved or feedback-driven systems, where applicable

All submissions should prioritize methodological innovation, rigor, and reproducibility, with clear relevance to experimental, theoretical, or computational neuroscience. Studies that are primarily clinically oriented, disease-driven, or focused on medical outcomes are considered out of scope. Contributions may include Original Research, Methods articles, and other formats aligned with the aims of the Neuroscience Methods and Techniques section.