  Objectives: Functional connectivity triggered by naturalistic stimulus (e.g.,
movies) and machine learning techniques provide a great insight in exploring
the brain functions such as fluid intelligence. However, functional
connectivity are considered to be multi-layered, while traditional machine
learning based on individual models not only are limited in performance, but
also fail to extract multi-dimensional and multi-layered information from brain
network. Methods: In this study, inspired by multi-layer brain network
structure, we propose a new method namely Weighted Ensemble-model and Network
Analysis, which combines the machine learning and graph theory for improved
fluid intelligence prediction. Firstly, functional connectivity analysis and
graphical theory were jointly employed. The functional connectivity and
graphical indices computed using the preprocessed fMRI data were then all fed
into auto-encoder parallelly for feature extraction to predict the fluid
intelligence. In order to improve the performance, tree regression and ridge
regression model were automatically stacked and fused with weighted values.
Finally, layers of auto-encoder were visualized to better illustrate the
connectome patterns, followed by the evaluation of the performance to justify
the mechanism of brain functions. Results: Our proposed methods achieved best
performance with 3.85 mean absolute deviation, 0.66 correlation coefficient and
0.42 R-squared coefficient, outperformed other state-of-the-art methods. It is
also worth noting that, the optimization of the biological pattern extraction
was automated though the auto-encoder algorithm. Conclusion: The proposed
method not only outperforming the state-of-the-art reports, but also able to
effectively capturing the biological patterns from functional connectivity
during naturalistic movies state for potential clinical explorations.
