Mei-Ling Wang, Wei Shao, Xiao-Ke Hao, Dao-Qiang Zhang. Machine Learning for Brain Imaging Genomics Methods: A Review. Machine Intelligence Research, vol. 20, no. 1, pp.57-78, 2023. https://doi.org/10.1007/s11633-022-1361-0
Citation: Mei-Ling Wang, Wei Shao, Xiao-Ke Hao, Dao-Qiang Zhang. Machine Learning for Brain Imaging Genomics Methods: A Review. Machine Intelligence Research, vol. 20, no. 1, pp.57-78, 2023. https://doi.org/10.1007/s11633-022-1361-0

Machine Learning for Brain Imaging Genomics Methods: A Review

doi: 10.1007/s11633-022-1361-0
More Information
  • Author Bio:

    Mei-Ling Wang received the M. Sc. degree in information and communication engineering from Nanjing University of Information Science and Technology, China in 2016, and the Ph. D. degree in computer science and technology from Nanjing University of Aeronautics and Astronautics, China in 2020. She is currently a postdoctor with College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China.Her research interests include machine learning and brain imaging genetics.E-mail: mely@nuaa.edu.cnORCID iD: 0000-0001-6569-2798

    Wei Shao received the B. Sc. and M. Sc. degrees in information and computing science from Nanjing University of Technology, China in 2009 and 2012, respectively, and the Ph. D. degree in software engineering from Nanjing University of Aeronautics and Astronautics, China in 2018. He is currently an associate professor with College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China.His research interests include machine learning and bioinformatics.E-mail: 527606857@qq.comORCID iD: 0000-0003-1476-2068

    Xiao-Ke Hao received the B. Sc. and M. Sc. degrees in computer science and technology from Nanjing University of Information Science and Technology, China in 2009 and 2012, respectively, and the Ph. D. degree in computer science and technology from Nanjing University of Aeronautics and Astronautics, China in 2017. He is currently an associate professor with School of Artificial Intelligence, Hebei University of Technology, China. He has published over 20 scientific articles in refereed journals such as IEEE Transactions on Image Processing, Medical Image Analysis, Bioinformatics. He is a member of the Artificial Intelligence and Pattern Recognition Society of the China Computer Federation (CCF).His research interests include machine learning, pattern recognition and medical image analysis.E-mail: haoxiaoke@hebut.edu.cnORCID iD: 0000-0003-3281-3340

    Dao-Qiang Zhang received the B. Sc. and Ph. D. degrees in computer science from Nanjing University of Aeronautics and Astronautics (NUAA), China in 1999 and 2004, respectively. He joined Department of Computer Science and Engineering of NUAA, as a lecturer in 2004, and is a professor at present. He has published over 200 scientific articles in refereed international journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Medical Imaging, IEEE Transactions on Image Processing, Neuroimage, Human Brain Mapping, and Medical Image Analysis, and conference proceedings such as IJCAI, AAAI, NIPS, CVPR, MICCAI, and KDD, with 12 000+ citations by Google Scholar. He was nominated for the National Excellent Doctoral Dissertation Award of China in 2006, and won the Best Paper Award and the Best Student Award of several international conferences such as PRICAI′06, STMI′12 and BICS′16, etc. He has served as a program committee member for some international conferences like IJCAI, AAAI, NIPS, MICCAI, SDM, PRICAI, ACML, etc. He is a member of the Machine Learning Society of the Chinese Association of Artificial Intelligence (CAAI), and the Artificial Intelligence and Pattern Recognition Society of the China Computer Federation (CCF).His research interests include machine learning, pattern recognition, data mining and medical image analysis.E-mail: dqzhang@nuaa.edu.cn (Corresponding author)ORCID iD: 0000-0002-5658-7643

  • Received Date: 2022-04-16
  • Accepted Date: 2022-08-01
  • In the past decade, multimodal neuroimaging and genomic techniques have been increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotypic measures derived from structural and functional brain imaging. This technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in humans. It is well known that machine learning is a powerful tool in the data-driven association studies, which can fully utilize priori knowledge (intercorrelated structure information among imaging and genetic data) for association modelling. In addition, the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is explored. In this paper, the related background and fundamental work in imaging genomics are first reviewed. Then, we show the univariate learning approaches for association analysis, summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning, and present methods for joint association analysis and outcome prediction. Finally, this paper discusses some prospects for future work.

     

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