Neurocognitive modeling of latent memory processes reveals reorganization of hippocampal-cortical circuits underlying learning and efficient strategies

Abstract

Efficient memory-based problem-solving strategies are a cardinal feature of expertise across a wide range of cognitive domains in childhood. However, little is known about the neurocognitive mechanisms that underlie the acquisition of efficient memory-based problem-solving strategies. Here we develop, to the best of our knowledge, a novel neurocognitive process model of latent memory processes to investigate how cognitive training designed to improve children’s problem-solving skills alters brain network organization and leads to increased use and efficiency of memory retrieval-based strategies. We found that training increased both the use and efficiency of memory retrieval. Functional brain network analysis revealed training-induced changes in modular network organization, characterized by increase in network modules and reorganization of hippocampal-cortical circuits. Critically, training-related changes in modular network organization predicted performance gains, with emergent hippocampal, rather than parietal cortex, circuitry driving gains in efficiency of memory retrieval. Our findings elucidate a neurocognitive process model of brain network mechanisms that drive learning and gains in children’s efficient problem-solving strategies.

Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism

“…Despite similar overall patterns of neural representational plasticity across the whole brain, including regions typically associated with math learning, our analysis of brain-behavioral relations revealed different patterns of relationship between NRP and learning between the two groups. Results from TD children revealed that training-related gains are accompanied by greater neural plasticity in brain systems associated with memory and quantity representation, instantiated in the MTL and IPS (Arsalidou et al, 2018;Dehaene & Cohen, 1997;Menon, 2015;Wu et al, 2009), as well as lateral occipital and frontal regions, a finding that is broadly consistent with previous reports (Jolles et al, 2016;Rosenberg-Lee et al, 2018;Supekar, Chang, et al, 2021) (Banker et al, 2021;Hogeveen et al, 2020;Schipul et al, 2012;Schumann et al, 2004;Welchew et al, 2005). Moreover, such atypical relations were not observed in the MTL for problems that were not trained in children with ASD, confirming the functional specificity of our observations in training-induced learning.…”

Foundational Number Sense Training Gains Are Predicted by Hippocampal–Parietal Circuits

“…Additional analysis confirmed that IPS regions identified from the current study overlap with the left IPS region identified from Neurosynth based meta-analysis, using the term "arithmetic" as defined in a previous study (Supekar et al, 2021) (Figures 5A-B), which indicates that the IPS region identified from our whole brain analysis converges with the region previously shown to be involved in math cognition. Finally, when using the IPS region identified from Neurosynth based meta-analysis, the association between hippocampal-left IPS circuits and learning in response to number sense training remains significant (left hippocampus-left IPS: ρ = .35, p = .011; right hippocampus-left IPS: ρ = .30, p = .03; Figures 5C-D).…”

“…Recent studies have begun to uncover a crucial role for the hippocampal memory system in mathematical skill acquisition Supekar, Chang, Mistry, Iuculano, & Menon, 2021). Most previous studies of mathematical cognition have focused on cortical regions, most notably the posterior parietal cortex; consequently, the role of medial temporal lobe regions in mathematical cognition and learning has received less attention because of this cortico-centric focus.…”

Cognitive training enhances growth mindset in children through plasticity of cortico-striatal circuits

“…A total of 14 (out of 52) participants were excluded for the following reasons: (i) 12 participants did not have high-quality fMRI data at pre- and/or post-visits; (ii) 1 participant was excluded due to low task accuracy (<40%) in the fMRI task at both visits; and (iii) 1 participant was excluded due to invalid or incomplete neuropsychological assessments at post-visit. The sample size in the training group ( n = 38) was determined adequate (estimated power > 83%) for brain-behavior analysis based on previous fMRI studies of tutoring-based intervention in children (range of Cohen’s d : 1.0–1.2) 54 , 55 (for more details on observed effect sizes and power, see also Supplementary Results ). After applying the same exclusion criteria, a total of 17 participants with high-quality fMRI data were included in the control group.…”