A Python Toolbox for Connectome-Based Predictive Modeling

Confound-Corrected Connectome-Based Predictive Modelling is a Python package for performing connectome-based predictive modeling (CPM). This toolbox is designed for researchers in neuroscience and psychiatry, providing robust methods for building predictive models based on structural or functional connectome data. It emphasizes replicability, interpretability, and flexibility, making it a valuable tool for analyzing brain connectivity and its relationship to behavior or clinical outcomes. The tool is accessible via https://github.com/wwu-mmll/confound_corrected_cpm .

Research Project

CPM is a machine learning framework that leverages the brain’s connectivity patterns to predict individual differences in behavior, cognition, or clinical status. By identifying key edges in the connectome, CPM creates models that link connectivity metrics with target variables (e.g., clinical scores). This approach is particularly suited for studying complex relationships in neuroimaging data and developing interpretable predictive models.