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<p>Developmental dyscalculia (DD), a learning disorder that affects one’s ability to work with numerical information and perform calculations, presents significant challenges for children acquiring mathematical skills. Recent research has provided a robust understanding of the behavioral and cognitive profile of DD, but its neurobiological underpinnings remain poorly understood. In this study, we use resting-state functional connectivity (rsFC) to investigate the neural differences between third graders with DD and typically achieving (TA) peers (DD = 30, TA = 37, mean age = 9.04 y). We employed two complementary analytical approaches: 1) a seed-based functional connectivity analysis to assess connectivity between a priori regions of interest (ROIs)—subregions of the intraparietal sulcus (IPS), angular gyrus, and the hippocampus—and the rest of the brain, and 2) a modified whole-brain, connectome-based predictive modeling approach to detect DD based on brain connectivity patterns. The seed-based connectivity analysis revealed greater functional connectivity for the TA group between the bilateral IPS and left hippocampal ROIs and frontal structures. Our whole-brain classification approach achieved a mean accuracy of 0.671 and an AUC of 0.816 and identified 14 brain connections that consistently classified the TA and DD groups. Our findings point to network-level differences underlying DD in brain regions previously implicated in mathematical cognition and offer a novel, data-driven approach to identifying differences in brain connectivity associated with DD.</p>

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Neural connectivity in developmental dyscalculia: a resting-state fMRI study using ROI-based and connectome-based approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月20日

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  • Omair A. Khan,
  • Lien Peters,
  • Fu Yu Kwok,
  • Andrew Lynn,
  • Daniel Ansari,
  • Eric D. Wilkey

摘要

Developmental dyscalculia (DD), a learning disorder that affects one’s ability to work with numerical information and perform calculations, presents significant challenges for children acquiring mathematical skills. Recent research has provided a robust understanding of the behavioral and cognitive profile of DD, but its neurobiological underpinnings remain poorly understood. In this study, we use resting-state functional connectivity (rsFC) to investigate the neural differences between third graders with DD and typically achieving (TA) peers (DD = 30, TA = 37, mean age = 9.04 y). We employed two complementary analytical approaches: 1) a seed-based functional connectivity analysis to assess connectivity between a priori regions of interest (ROIs)—subregions of the intraparietal sulcus (IPS), angular gyrus, and the hippocampus—and the rest of the brain, and 2) a modified whole-brain, connectome-based predictive modeling approach to detect DD based on brain connectivity patterns. The seed-based connectivity analysis revealed greater functional connectivity for the TA group between the bilateral IPS and left hippocampal ROIs and frontal structures. Our whole-brain classification approach achieved a mean accuracy of 0.671 and an AUC of 0.816 and identified 14 brain connections that consistently classified the TA and DD groups. Our findings point to network-level differences underlying DD in brain regions previously implicated in mathematical cognition and offer a novel, data-driven approach to identifying differences in brain connectivity associated with DD.