Generative AI for brain image computing and brain network computing: a review

Changwei Gong; Changhong Jing; Xuhang Chen; Chi Man Pun; Guoli Huang; Ashirbani Saha; Martin Nieuwoudt; Han-Xiong Li; Yong Hu; Shuqiang Wang

doi:10.3389/fnins.2023.1203104

Generative AI for brain image computing and brain network computing: a review

Front Neurosci. 2023 Jun 13:17:1203104. doi: 10.3389/fnins.2023.1203104. eCollection 2023.

Authors

Changwei Gong^{1

2}, Changhong Jing^{1

2}, Xuhang Chen^{1

3}, Chi Man Pun³, Guoli Huang¹, Ashirbani Saha⁴, Martin Nieuwoudt⁵, Han-Xiong Li⁶, Yong Hu⁷, Shuqiang Wang^{1

2}

Affiliations

¹ Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
² Department of Computer Science, University of Chinese Academy of Sciences, Beijing, China.
³ Department of Computer and Information Science, University of Macau, Macau, China.
⁴ Department of Oncology and School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.
⁵ Institute for Biomedical Engineering, Stellenbosch University, Stellenbosch, South Africa.
⁶ Department of Systems Engineering, City University of Hong Kong, Hong Kong, China.
⁷ Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China.

Abstract

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial.

Keywords: brain imaging; brain network; diffusion model; generative adversarial network; generative models; variational autoencoder.

Publication types

Review

Grants and funding

This work was supported by the National Natural Science Foundations of China under Grant 62172403, the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, the Excellent Young Scholars of Shenzhen under Grant RCYX20200714114641211, and Shenzhen Key Basic Research Projects under Grant JCYJ20200109115641762.