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This chapter outlines a flexible connectome-based predictive modeling method that is optimised for large neuroimaging datasets via the use of parallel computing and by adding the capability to account for possible site- and scanner-related heterogeneity in multi-site neuroimaging datasets. We present the decision points that need to be made when conducting a connectome-based predictive modeling analysis and we provide full code to conduct an analysis on public data. To date, connectome-based predictive modeling has been applied to predict different cognitive and behavioral phenotypes with many studies reporting accurate predictions that generalized to external datasets.

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Studying the Connectome at a Large Scale

  • 实验方案

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Rory Boyle,
  • Yihe Weng

摘要

This chapter outlines a flexible connectome-based predictive modeling method that is optimised for large neuroimaging datasets via the use of parallel computing and by adding the capability to account for possible site- and scanner-related heterogeneity in multi-site neuroimaging datasets. We present the decision points that need to be made when conducting a connectome-based predictive modeling analysis and we provide full code to conduct an analysis on public data. To date, connectome-based predictive modeling has been applied to predict different cognitive and behavioral phenotypes with many studies reporting accurate predictions that generalized to external datasets.