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Generative artificial intelligence-enabled dynamic detection of rat nicotine-related circuits

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Abstract

Nicotine addiction circuits involve integrating specific brain regions that alter to frequent smoking. Detection of these circuits via fMRI contributes to understanding addiction-related mechanisms. Identification of the functional circuits and networks altered by nicotine is essential to improve the treatment of nicotine addiction. However, analyzing fMRI data and detecting functional addiction circuits still have challenges. In this work, we developed a generative AI-enabled framework, rat addiction-related circuits detection platform (RADP), to detect nicotine-related circuits. It has an end-to-end pipeline: functional imaging data acquisition from neurobiological experiments, computational modeling for brain networks, and a novel generative model including spatiotemporal transformer auto-encoder (STA) and dynamic circuits analysis. The proposed spatiotemporal representation contrasting trains the encoder of STA to contrastively capture representations between the addictive and the control groups. Experimental results indicate that the framework can efficiently detect the verified addiction circuits and discover the unknown but significant circuits. Moreover, RADP can be served as a general tool which can be extended to other brain circuits.

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Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

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

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Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Changwei Gong, Changhong Jing, Ye Li & Shuqiang Wang

  2. Shenzhen Key Laboratory of Drug Addiction, Shenzhen Neher Neural Plasticity Laboratory, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, 518055, China

    Xin-an Liu & Zuxin Chen

  3. Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China

    Xin-an Liu & Zuxin Chen

  4. Department of Radiology, Icahn School of Medicine at Mount Sinai, Translational and Molecular Imaging Institute, New York, NY, 10029, USA

    Victoria X. Wang & Cheuk Ying Tang

  5. Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Paul J. Kenny

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  1. Changwei Gong
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Gong, C., Jing, C., Liu, Xa. et al. Generative artificial intelligence-enabled dynamic detection of rat nicotine-related circuits. Neural Comput & Applic 36, 4693–4707 (2024). https://doi.org/10.1007/s00521-023-09307-0

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