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A recursive partitioning approach for subgroup identification in brain–behaviour correlation analysis

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Abstract

In neural correlates studies, the goal is to understand the brain–behaviour relationship characterized by correlation between brain activation responses and human behaviour measures. Such correlation depends on subject-related covariates such as age and gender, so it is necessary to identify subgroups within the population that have different brain–behaviour correlations. The subgrouping is made by manual specification in current practice, which is inefficient and may ignore potential covariates whose effects are unknown in the literature. This study proposes a recursive partitioning approach, called correlation tree, for automatic subgroup identification in brain–behaviour correlation analysis. In constructing a correlation tree, the split variable at each node is selected through an unbiased variable selection method based on partial correlation test, and then, the optimal cutpoint of the selected split variable is determined through exhaustive search under an objective function. Three types of meaningful objective functions are considered to meet various practical needs. Results of simulation and application to real data from optical brain imaging demonstrate effectiveness of the proposed approach.

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Acknowledgement

The authors acknowledge Dr. Mary Cazzell at Cook Children’s Medical Center for her help on data collection.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA

    Doowon Choi & Li Zeng

  2. Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA

    Lin Li

  3. Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA

    Hanli Liu

Authors
  1. Doowon Choi
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  2. Lin Li
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  3. Hanli Liu
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  4. Li Zeng
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Correspondence to Li Zeng.

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Choi, D., Li, L., Liu, H. et al. A recursive partitioning approach for subgroup identification in brain–behaviour correlation analysis. Pattern Anal Applic 23, 161–177 (2020). https://doi.org/10.1007/s10044-018-00775-y

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