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Abstract A critical goal of cognitive neuroscience is to predict behavior from neural structure and function, thereby providing crucial insights into who might benefit from clinical and/or educational interventions. Across development, the strength of functional connectivity among a distributed set of brain regions is associated with children’s math skills. Therefore, in the present study we use connectome-based predictive modeling to investigate whether functional connectivity during numerical processing and at rest “predicts” children’s math skills (N = 31, Mage = 9.21 years, 14 Female). Overall, we found that functional connectivity during symbolic number comparison and rest, but not during nonsymbolic number comparison, predicts children’s math skills. Each task revealed a largely distinct set of predictive connections distributed across canonical brain networks and major brain lobes. Most of these predictive connections were negatively correlated with children’s math skills so that weaker connectivity predicted better math skills. Notably, these predictive connections were largely nonoverlapping across task states, suggesting children’s math abilities may depend on state-dependent patterns of network segregation and/or regional specialization. Furthermore, the current predictive modeling approach moves beyond brain–behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.
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Described neural connections enhance classroom learning of neuroanatomy
Abstract Advances in brain imaging have led to a paradigm shift in neuroscience research, moving from focusing on individual brain structures to investigating neural networks and connections. However, neuroanatomy education still tends to concentrate on discrete brain regions. Two separate experiments in undergraduate neuroscience courses investigated whether incorporating neural connectivity into neuroanatomy education would enhance learning. Students in each experiment learned to identify brain structures through computer‐based training sessions that provided text‐based narrative feedback about neural connections, followed by final memory tests after a 1‐month delay. The first experiment included 30 students and demonstrated a long‐term memory benefit associated with described neural connections, showing a medium effect size (p = 0.01,d = 0.54) comparable to the established retrieval practice effect for enhancing long‐term memory (p = 0.03,d = 0.47). The second experiment replicated the benefits of described neural connections with a small effect size (p = 0.005,d = 0.28) in a larger sample of 122 students across classrooms at two universities. Furthermore, students remembered the functional outcomes of neural connections from training (p < 0.001,d = 0.46), and this generalized to clinical applications (p = 0.009,d = 0.27). In contrast, categorizing brain areas without describing neural connections (as is commonly done in introductory neuroscience textbook chapters) did not benefit either memory or generalization. Findings demonstrate that leveraging the connectivity paradigm shift in neuroscience research can enhance neuroanatomy education. Emphasizing neural connections and their functional outcomes helps simplify neuroanatomy and improve understanding and retention.
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- Award ID(s):
- 2315440
- PAR ID:
- 10600213
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Anatomical Sciences Education
- Volume:
- 18
- Issue:
- 7
- ISSN:
- 1935-9772
- Format(s):
- Medium: X Size: p. 642-656
- Size(s):
- p. 642-656
- Sponsoring Org:
- National Science Foundation
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