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International Conference on Brain Informatics

BI 2021: Brain Informatics pp 299–309Cite as

An Image-Enhanced Topic Modeling Method for Neuroimaging Literature

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12960))

Abstract

Topic modeling based on neuroimaging literature is an important approach to aggregate world-wide research findings for decoding brain cognitive mechanism, as well as diagnosis and treatment of brain and mental diseases, artificial intelligence researches, etc. However, existing neuroimaging literature mining only focused on texts and neglects brain images which contain a large amount of topic information. Following the writing and reading habits combining images with texts, we present in this paper an image-enhanced LDA (Latent Dirichlet Allocation), which extracts literature topics from both neuroimaging images and full texts. Combining topics from fMRI brain regions activation images with topics from full texts to model neuroimaging literatures more accurately. On the one hand, topics related brain cognitive mechanism can be pertinently extracted from activated brain images and their descriptions. On the other hand, topics from activated brain images can be integrated with topics from full text to model neuroimaging literature more accurately. The experiments based on actual data has preliminarily proved effectiveness of proposed method.

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Acknowledgements

The work is supported by the JSPS Grants-in-Aid for Scientific Research of Japan (19K12123), the National Natural Science Foundation of China (61420106005), the National Basic Research Program of China (2014CB744600), the National Key Research and Development Project of China (2020YFC2007300, 2020YFC2007302) and the Key Research Project of Academy for Multi-disciplinary Studies of Capital Normal University (JCKXYJY2019019), the National Key Research and Development Program of China (Grant No. 2020YFB2104402).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Lianfang Ma, Jianhui Chen & Ning Zhong

  2. Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, 100124, China

    Lianfang Ma, Jianhui Chen & Ning Zhong

  3. Beijing Key Laboratory of MRI and Brain Informatics, Beijing, 100124, China

    Lianfang Ma, Jianhui Chen & Ning Zhong

  4. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Gunma, 371-0816, Japan

    Ning Zhong

Authors
  1. Lianfang Ma
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  2. Jianhui Chen
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  3. Ning Zhong
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Corresponding author

Correspondence to Jianhui Chen .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Dhaka, Bangladesh

    M Shamim Kaiser

  3. University of Padua, Padua, Italy

    Stefano Vassanelli

  4. Tsinghua University, Beijing, China

    Qionghai Dai

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Ma, L., Chen, J., Zhong, N. (2021). An Image-Enhanced Topic Modeling Method for Neuroimaging Literature. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds) Brain Informatics. BI 2021. Lecture Notes in Computer Science(), vol 12960. Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_28

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  • DOI: https://doi.org/10.1007/978-3-030-86993-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86992-2

  • Online ISBN: 978-3-030-86993-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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