TY - GEN
T1 - Visualizing functional network connectivity difference between middle adult and older subjects using an explainable machine-learning method
AU - Sendi, Mohammad S.E.
AU - Chun, Ji Ye
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - explainable machine learning
KW - functional network connectivity
KW - machine learning classification
KW - resting-state fMRI
UR - http://www.scopus.com/inward/record.url?scp=85099585377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099585377&partnerID=8YFLogxK
U2 - 10.1109/BIBE50027.2020.00162
DO - 10.1109/BIBE50027.2020.00162
M3 - Conference contribution
AN - SCOPUS:85099585377
T3 - Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
SP - 955
EP - 960
BT - Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
Y2 - 26 October 2020 through 28 October 2020
ER -