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Objectives: Functional connectivity triggered by naturalistic stimulus (e.g.,movies) and machine learning techniques provide a great insight in exploringthe brain functions such as fluid intelligence. However, functionalconnectivity are considered to be multi-layered, while traditional machinelearning based on individual models not only are limited in performance, butalso fail to extract multi-dimensional and multi-layered information from brainnetwork. Methods: In this study, inspired by multi-layer brain networkstructure, we propose a new method namely Weighted Ensemble-model and NetworkAnalysis, which combines the machine learning and graph theory for improvedfluid intelligence prediction. Firstly, functional connectivity analysis andgraphical theory were jointly employed. The functional connectivity andgraphical indices computed using the preprocessed fMRI data were then all fedinto auto-encoder parallelly for feature extraction to predict the fluidintelligence. In order to improve the performance, tree regression and ridgeregression model were automatically stacked and fused with weighted values.Finally, layers of auto-encoder were visualized to better illustrate theconnectome patterns, followed by the evaluation of the performance to justifythe mechanism of brain functions. Results: Our proposed methods achieved bestperformance with 3.85 mean absolute deviation, 0.66 correlation coefficient and0.42 R-squared coefficient, outperformed other state-of-the-art methods. It isalso worth noting that, the optimization of the biological pattern extractionwas automated though the auto-encoder algorithm. Conclusion: The proposedmethod not only outperforming the state-of-the-art reports, but also able toeffectively capturing the biological patterns from functional connectivityduring naturalistic movies state for potential clinical explorations.

Weighted Ensemble-model and Network Analysis: A method to predict fluid intelligence via naturalistic functional connectivity