4.5 Article

Neuroanatomical differences in the memory systems of intellectual giftedness and typical development

Journal

BRAIN AND BEHAVIOR
Volume 11, Issue 11, Pages -

Publisher

WILEY
DOI: 10.1002/brb3.2348

Keywords

connectivity; intellectual giftedness; learning strategies; memory systems; neuroanatomy

Funding

  1. University of California, Los Angeles Staglin Center for Cognitive Neuroscience

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This study used MRI to compare brain structural differences between typically developing children and intellectually gifted children. Results showed that intellectually gifted children had larger subcortical structures and stronger connectivity in regions associated with explicit memory, while typically developing children had more connected, larger subcortical structures in regions associated with implicit memory. These findings suggest different neurodevelopmental trajectories and learning strategies in children with exceptional intellectual abilities.
Introduction Studying neuro-structural markers of intellectual giftedness (IG) will inform scientific understanding of the processes helping children excel academically. Methods Structural and diffusion-weighted MRI was used to compare regional brain shape and connectivity of 12 children with average to high average IQ and 18 IG children, defined as having IQ greater than 145. Results IG had larger subcortical structures and more robust white matter microstructural organization between those structures in regions associated with explicit memory. TD had more connected, larger subcortical structures in regions associated with implicit memory. Conclusions It was found that the memory systems within brains of children with exceptional intellectual abilities are differently sized and connected compared to the brains of typically developing children. These different neurodevelopmental trajectories suggest different learning strategies. A spectrum of intelligence types is envisioned, facilitated by different ratios of implicit and explicit system, which was validated using a large external dataset.

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