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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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NeuroQuery, comprehensive meta-analysis of human brain mapping
Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept. Thus, large-scale meta-analyses only tackle single terms that occur frequently. We propose a new paradigm, focusing on prediction rather than inference. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. We capture the relationships and neural correlates of 7547 neuroscience terms across 13 459 neuroimaging publications. The resulting meta-analytic tool, neuroquery.org, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain.
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- Award ID(s):
- 1649658
- PAR ID:
- 10159416
- Date Published:
- Journal Name:
- eLife
- Volume:
- 9
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.53385
