PLOTLYBRAIN: AN INTERACTIVE FRAMEWORK FOR ATLAS-BASED VISUALIZATION OF QUANTITATIVE HISTOLOGICAL TRAITS

Large-scale histological datasets aligned to brain atlases provide rich, region-resolved quantitative information, but their interpretation is often limited by static and inflexible visualization approaches. Here, we introduce PlotlyBrain, an interactive Python-based framework designed to visualize atlas-registered quantitative traits derived from histological data. PlotlyBrain takes region-wise outputs from the QUINT workflow and computes complementary metrics capturing both the relative abundance of a signal (z-scored object counts or areas) and its consistency across animals (frequency scores). These metrics are mapped onto the Allen Mouse Brain Common Coordinate Framework, with each anatomical region rendered according to its atlas-defined area and colored using customizable continuous scales. The framework provides a Plotly-based interactive interface that enables dynamic exploration of quantitative traits across brain regions, rostro–caudal levels, experimental groups, and markers. Users can generate publication-ready static images, export figures at high resolution, or render atlas-aligned data in three dimensions for exploratory analysis and presentation. PlotlyBrain is modular and marker-agnostic, allowing it to be applied to diverse atlas-based datasets beyond a single experimental domain. We demonstrate the utility of PlotlyBrain using region-resolved quantification of intracellular amyloid-β, extracellular amyloid-β plaques, and hyperphosphorylated tau across aging in the 3xTg-AD mouse model. This example illustrates how the framework facilitates intuitive comparison of signal distribution, regional enrichment, and cross-animal consistency in complex histological datasets. PlotlyBrain bridges quantitative atlas-based analysis and interactive visualization, improving accessibility and interpretability of high-dimensional neuroanatomical data.
Funding: PID2022-141733NB-I00 fom the MCIU/ AEI /10.13039/501100011033/FEDER, UE; CIAICO/2023/041 from the Conselleria de Educación, Universidades y Empleo (Generalitat Valenciana).