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Abstract #0435

Prediction of Age using Resting-State Functional & Effective Connectivity

Zhihao Li1, John A. Sexton1, Gopikrishna Deshpande2, Xiaoping Hu1

1Biomedical Engineering, Emory University & Georgia Institute of Technology, Atlanta, GA, USA; 2Electrical & Computer Engineering, Auburn University, Auburn, AL, USA

Using resting-state fMRI, support-vector-machine based multivariate pattern analysis recently shows capability of accurate predictions about brain maturity. However, with more information about inter-regional causal influences, effective connectivity may possess more power in characterizing the development of neural networks. The present study compared age prediction of these two approaches and showed a relatively better performance with effective connectivity.