NaviGraph: A graph-based framework for multimodal analysis of spatial decision-making

Abstract

Understanding spatial decision-making requires interpreting multimodal data streams through a unified analytical lens that relates them to one another and to the layout of the underlying task. We developed NaviGraph (Navigation on the Graph), an open-source framework that formalizes spatial tasks as graphs to provide a topological scaffold for data integration. This approach solves the challenge of aligning disparate data streams while standardizing spatial analysis across diverse laboratories and behavioral paradigms. NaviGraph transforms decision points into nodes and paths into edges, enabling the computation of both conventional and graph-based metrics, such as unique node exploration and trajectory efficiency. We demonstrate its utility and enhanced sensitivity through a complex memory task, where graph-derived metrics revealed subtle recall impairments in female Apolipoprotein E {varepsilon}4 (ApoE4) mice - the primary genetic risk factor for Alzheimers disease - that were undetectable by conventional measures alone. We further illustrate multimodal alignment by mapping retrosplenial cortex calcium imaging and head orientation onto the graph structure, offering a topological perspective on decision-point dynamics. With its modular, plugin-based architecture, NaviGraph provides a standardized environment for the exploration of multimodal data across diverse spatial paradigms.