Exploitation of Mapper Algorithm in Neuroimaging Applications: A Novel Framework for Outcomes Prediction

Topological Data Analysis (TDA) represents a pioneering methodology for revealing intricate structures within complex datasets. This study introduces a novel framework for leveraging the Mapper algorithm in neuroimaging studies. The proposed framework involves mapping new independent test samples onto a pre-constructed train graph, thereby harnessing embedded topological features to derive novel insights about test data. Validation of the framework employs a neuroimaging dataset sourced from the Human Connectome Project (HCP), encompassing white matter brain features, and includes practical applications for predicting categorical and continuous outcomes. The results validate the framework efficacy in transferring knowledge from train data to predict unseen samples, underscoring its potential across diverse neuroimaging applications. This research highlights the potential of the Mapper-based TDA framework in neuroimaging, paving the way for its application across diverse neuroscience domains to extract clinically relevant features, improve predictive accuracy, and enhance patient treatment strategies. By discerning intricate patterns within high-dimensional patient data, this approach enables precise diagnostics and personalized treatment strategies, contributing to more accurate disease profiling and optimizing therapeutic interventions in personalized medicine.