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Application of Supervised Machine Learning to Extract Brain Connectivity Information from Neuroscience Research Articles

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Abstract

Understanding the complex connectivity structure of the brain is a major challenge in neuroscience. Vast and ever-expanding literature about neuronal connectivity between brain regions already exists in published research articles and databases. However, with the ever-expanding increase in published articles and repositories, it becomes difficult for a neuroscientist to engage with the breadth and depth of any given field within neuroscience. Natural Language Processing (NLP) techniques can be used to mine ‘Brain Region Connectivity’ information from published articles to build a centralized connectivity resource helping neuroscience researchers to gain quick access to research findings. Manually curating and continuously updating such a resource involves significant time and effort. This paper presents an application of supervised machine learning algorithms that perform shallow and deep linguistic analysis of text to automatically extract connectivity between brain region mentions. Our proposed algorithms are evaluated using benchmark datasets collated from PubMed and our own dataset of full text articles annotated by a domain expert. We also present a comparison with state-of-the-art methods including BioBERT. Proposed methods achieve best recall and \(F_2\) scores negating the need for any domain-specific predefined linguistic patterns. Our paper presents a novel effort towards automatically generating interpretable patterns of connectivity for extracting connected brain region mentions from text and can be expanded to include any other domain-specific information.

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Data Availability

Models generated on the benchmark corpus WhiteText, Supplementary material including the datasets are made available at https://github.com/ashika55/BRConnExt.

Notes

  1. http://brainarchitecture.org/text-mining

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Authors and Affiliations

  1. Center for Artificial Intelligence and Robotics, DRDO Complex, Bangalore, 560093, India

    Ashika Sharma

  2. Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, 600036, India

    Ashika Sharma, Sutanu Chakraborti & P. Sreenivasa Kumar

  3. Center for Computational Brain Research, IIT Madras, Chennai, 600036, India

    Jaikishan Jayakumar

  4. Cold Spring Harbour Laboratory, Cold Spring Harbour, New York, 11724, USA

    Partha P. Mitra

Authors
  1. Ashika Sharma
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  2. Jaikishan Jayakumar
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  3. Partha P. Mitra
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  4. Sutanu Chakraborti
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  5. P. Sreenivasa Kumar
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Correspondence to Ashika Sharma.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

Code availability

Software application, ‘ConnExt1’ can be accessed at http://brainarchitecture.org/text-mining.

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Sharma, A., Jayakumar, J., Mitra, P.P. et al. Application of Supervised Machine Learning to Extract Brain Connectivity Information from Neuroscience Research Articles. Interdiscip Sci Comput Life Sci 13, 731–750 (2021). https://doi.org/10.1007/s12539-021-00443-6

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  • DOI: https://doi.org/10.1007/s12539-021-00443-6

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