Unified Filtered Operator Analysis of Multi-Scale Brain Imaging Statistics: Experimental Validation in PLOS ONE ROI Dataset

We apply the unified filtered operator to experimental multi-scale statistics from brain imaging data. Mean Z-score (μ), standard deviation (σ), and p-value are analyzed for brain regions of interest (ROIs). The unified operator selectively detects statistically or physically significant changes (σ > 1 or p < 0.05), outperforming classical differentiation in noisy or homogeneous regions. Applied to real-world neurological imaging data (PLOS ONE dataset), this method enables robust and meaningful feature detection, offering improved interpretability in high-dimensional biomedical data. The author acknowledges the use of OpenAI’s language model for assistance in drafting certain sections of this workl Disclaimer on Reference Accuracy: The author acknowledges the use of OpenAI’s language model for assistance in drafting certain sections of this work. Due to the nature of generative language models, some reference years or citation details may be imprecise or outdated. All critical sources have been verified manually; however, readers are advised to cross-check dates if exact historical accuracy is required.