Weighted ensemble-model and network analysis: a method to predict fluid intelligence via naturalistic functional connectivity

Liu, Xiaobo and Yang, Su ORCID: https://orcid.org/0000-0002-6618-7483 (2021) Weighted ensemble-model and network analysis: a method to predict fluid intelligence via naturalistic functional connectivity. arXiv.org. (Submitted)

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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 network and graphical indices computed using the preprocessed fMRI data were then fed into auto-encoder parallelly for feature extraction to predict the fluid intelligence. In order to improve the performance, different models were automatically stacked and fused with weighted values. Finally, layers of auto-encoder were visualized to better illustrate the impacts, 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 common and biological pattern from functional connectivity during naturalistic movies state for potential clinical explorations.

Item Type: Article
Keywords: functional Magnetic Resonance Imaging, Functional connectivity, Weighted Ensemble-model and Network Analysis, fluid intelligence
Subjects: Computing
Depositing User: Su Yang
Date Deposited: 07 Jun 2021 09:04
Last Modified: 06 Feb 2024 16:05
URI: https://repository.uwl.ac.uk/id/eprint/7934


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