Neural predictors of individual differences in response to math tutoring in primary-grade school children

Abstract

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8-9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures. math learning | intervention | prediction | multimodal neuroimaging | educational neuroscience

Constructivist developmental theory is needed in developmental neuroscience

“…37 A similar conclusion is drawn from a study examining mathematical achievement, which shows that individual responses to 8 weeks of training can be predicted by brain anatomy and functional connectivity measures recorded before training; this is the connectivity and cortical indices of hippocampus and basal ganglia, but not of parietal areas-sites of learning typically engaged during mathematical problem solving. 38 These cognitive neuroscience findings can inform, and be informed by, developmental theorising; they support theories that explain learning in terms of brain-operator processes that are not contentor material-specific (e.g., visual-spatial or verbal), because these brain regions (basal ganglia, thalamus and hippocampus) have complex coordinating functions that apply across content domains. Problem solving and complex learning processes are clearly mediated in the brain through prefrontal-basal-gangliathalamus pathways.…”

The practical and principled problems with educational neuroscience.

“…A somewhat different result was reached by Supekar et al (2013) who carried out functional and structural MRI analyses on grade 3 children (aged 8-9) prior to an intensive eight-week one-on-one math tutoring programme that focused on conceptual aspects of number knowledge and speeded practice on counting strategies (conceptual and procedural skills, respectively). The critical finding was that hippocampal volume and intrinsic connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia predicted training gains, whereas various behavioral measures did not.…”

Neural circuits underlying mother’s voice perception predict social communication abilities in children

“…To examine the robustness and reliability of these particular brain connections for predicting social communication scores, we performed a support vector regression (SVR) analysis (24)(25)(26). Results showed that the strength of each of these brain connections was a reliable predictor of social communication function (left aSTS gPPI seed to left NAc: r = 0.62, P < 0.001; to right amygdala: r = 0.49, P = 0.004; to right hippocampus: r = 0.59, P < 0.001; to right fusiform: r = 0.54, P = 0.002; right pSTS gPPI seed to right OFC: r = 0.58, P < 0.001; to right AI: r = 0.66, P < 0.001; to right dACC: r = 0.66, P < 0.001).…”