A GMV-based model significantly predicts visuospatial working memory, with brain-region overlap with the arithmetic models¶

Claim · All claims
Evidence · 1 study · associational r2 · q2 · i2 medium
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i2 A GMV-based predictive model for visuospatial WM (spatial recall subtest) was significant (r = 0.3435) and overlapped in regions with both arithmetic predictive models. → Zhang 2024

Evidence¶

Zhang 2024¶

Zhang, D., Xie, Y., Wang, L. & Zhou, K. (2024). Structural and transcriptional signatures of arithmetic abilities in children. npj Science of Learning 9, 58. https://doi.org/10.1038/s41539-024-00270-6

q2 · i2 · associational · r2

LOOCV predictive modeling of visuospatial WM (AWMA-S spatial recall) in the same sample; the model "involves widely distributed brain regions" overlapping the subtraction and multiplication models (e.g., right transverse temporal sulcus), suggesting shared structural basis.

"For the visuospatial-WM predictive model, the correlation between observed and predicted scores was significant (r = 0.3435, the first permutation testing approach: Pperm = 0.0001; the second permutation testing approach: Pperm = 0.001; Supplementary Fig. 3a)."

Discussion¶

Related Claims¶

• Predicted arithmetic scores correlate with visuospatial working memory but show poor correlation with verbal working memory — related
• Gray matter volume significantly predicts children's multiplication skill in a cross-validated model — related
• Gray matter volume of widespread cortical regions significantly predicts children's subtraction skill in a cross-validated model — related
• Structural patterns of arithmetic ability correlate with transcriptional profiles enriched for transmembrane transport (subtraction) and synaptic signaling (multiplication)