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<p>We depart from the feed-forward approach of brain-behavior modeling, which identifies the functional brain connectivity networks associated with performance on external tests, and instead introduce a feedback approach that reveals the brain systems those external tests reflect. In fMRI data from <i>n</i> = 302 demographically and clinically diverse participants, we a priori define connectivity networks for six cognitive constructs and employ kernel ridge regression to quantify each network’s contribution to test performance. This approach provides a ranking of test scores according to the predictive power of each cognitive network, revealing which tests probe which brain networks. It further identifies combinations of measures that optimally probe predefined brain systems and evaluates how specific subtests influence composite scores, revealing when subset inclusion reinforces or weakens specific brain circuit and composite score relationships. This work opens an avenue of research by providing a framework for the development of test instruments guided by quantitative brain metrics.</p>

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Using connectome-based predictive models to reveal the systems standardized tests and clinical symptoms are reflecting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

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  • Anja Samardzija,
  • Xilin Shen,
  • Wenjing Luo,
  • Abigail S. Greene,
  • Saloni Mehta,
  • Fuyuze Tokoglu,
  • Jagriti Arora,
  • Scott W. Woods,
  • Rachel B. Katz,
  • Gerard Sanacora,
  • Vinod H. Srihari,
  • Dustin Scheinost,
  • R. Todd Constable

摘要

We depart from the feed-forward approach of brain-behavior modeling, which identifies the functional brain connectivity networks associated with performance on external tests, and instead introduce a feedback approach that reveals the brain systems those external tests reflect. In fMRI data from n = 302 demographically and clinically diverse participants, we a priori define connectivity networks for six cognitive constructs and employ kernel ridge regression to quantify each network’s contribution to test performance. This approach provides a ranking of test scores according to the predictive power of each cognitive network, revealing which tests probe which brain networks. It further identifies combinations of measures that optimally probe predefined brain systems and evaluates how specific subtests influence composite scores, revealing when subset inclusion reinforces or weakens specific brain circuit and composite score relationships. This work opens an avenue of research by providing a framework for the development of test instruments guided by quantitative brain metrics.