Visualizing functional network connectivity difference between middle adult and older subjects using an explainable machine-learning method

• Main Authors: Sendi, Mohammad S. E., Chun, Ji Ye, Calhoun, Vince D.
• Format: Conference Proceeding
• Language: English
• Subjects: Brain modeling; explainable machine learning; Feature extraction; functional network connectivity; Learning systems; machine learning classification; Radio frequency; Random forests; resting-state fMRI; Statistical learning; Visualization
• ISSN: 2471-7819
• DOI: 10.1109/BIBE50027.2020.00162

In this study, we classified older (63 years old) from middle adult (45-63 years old) subjects by estimating whole-brain functional network connectivity (FNC) including the connectivity among subcortical network (SCN), auditory network (ADN), sensorimotor network (SMN), visual sensory network (VSN), cognitive control network (CCN), default mode network (DMN), cerebellar network (CBN) from the adult subjects (n = 9394; 45-81 y). We used three tree-based classifiers, including random forest (RF), XGBoost, and CATBoost. Next, we leveraged the SHapley Additive exPlanations (SHAP) approach as an explainable feature learning method to model the difference between the brain connectivity of the old and middle adult subjects. Opposed to the conventional statistical learning, which typically assesses each feature separately, the explainable machine learning method used here offers a generalized model in the connectivity difference between older and middle adults. Based on this method, we found that all three models successfully differentiate middle adult adults from older adults based on wholebrain FNC. We also found that all brain networks contributed to the top 20 features selected by the SHAP method in all three models. We highlighted the role of the CCN and SNC in differentiating between these two groups.