Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification

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Brain, Cognition and Mental Health

Main article text

 

Introduction

The Proposed Method

Motivation

Model and algorithm

Experiments

Data acquisition and preprocessing

Experimental setting

Brain functional network estimation

Feature selection and classification

Results

Network visualization

Classification performance

Discussions

Parameter analysis

Sensitivity to network modelling parameters

Top discriminative features

Conclusion

Supplemental Information

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Lizhong Liang performed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

Zijian Zhu analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

Hui Su analyzed the data, prepared figures and/or tables, and approved the final draft.

Tianming Zhao analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

Yao Lu conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The original data are available in the Supplementary Files.

All the data used in the experiments, as well as the models generated and the source code are available at GitHub and Zenodo:

-https://github.com/zhaotm/NSGBFN.

- Liang, L. (2024). Neighborhood Structure-Guided Brain Functional Networks Estimation for MCI Identification. Zenodo. https://doi.org/10.5281/zenodo.10903092.

Funding

This work was supported by the China Department of Science and Technology under Key Grant 2023YFE0204300, by the R&D project of Pazhou Lab (HuangPu) under Grant 2023K0606, by the NSFC under Grant 62371476, Grant 12126610, Grant 82371917, Grant 81971691, Grant 81801809, Grant 81830052, Grant 81827802, and Grant U1811461, by the China Department of Science and Technology under Key Grant 210YBXM 2020109002, by the Guangzhou Science and Technology bureau under Grant 2023B03J1237, by the Science and Technology Innovative Project of Guangdong Province under Grant 2018B030312002, by Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University under grant 2020B1212060032, by Key-Area Research and Development Program of Guangdong Province under Grant 2021B0101190003. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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