All Publications

  • Causal dynamics of memory circuits in mathematical development from childhood to adulthood. Developmental cognitive neuroscience Liu, R., Chang, H., Zhang, Y., Menon, V. 2025; 76: 101628

    Abstract

    Mathematical cognition engages a distributed brain network, but the causal dynamics of information flow within it, particularly how memory circuits interact with other brain regions across development, remain unknown. We examined causal dynamic interactions in typically developing children and adolescents/young adults (AYA) using fMRI during three tasks involving mental arithmetic and symbolic and non-symbolic number comparison. Using multivariate dynamic state-space identification modeling, we found that causal dynamic interactions differed between children and AYA across all three tasks, especially during arithmetic processing. The left medial temporal lobe (MTL) served as a causal signaling hub in AYA across all three tasks, but not in children. The left angular gyrus (AG) maintained consistent hub-like properties during arithmetic task across development. Compared to AYA, children exhibited heightened causal interactions in both the MTL and AG. Moreover, network hub properties of these regions correlated with individual's mathematical achievement specifically during arithmetic processing. Together, we found that the MTL transitioned from heightened, context-dependent, interactions in childhood to a stable causal hub in adulthood, while the AG maintained as a hub during arithmetic processing across development. This dissociation between memory systems, coupled with their task-specific relationship to mathematical abilities, provides novel insights into how brain networks mature to support mathematical cognition.

    View details for DOI 10.1016/j.dcn.2025.101628

    View details for PubMedID 41092814

  • Brain-wide decoding of numbers and letters: Converging evidence from multivariate fMRI analysis and probabilistic meta-analysis. Cortex; a journal devoted to the study of the nervous system and behavior Liu, R., Chang, H., El-Said, D., Wassermann, D., Zhang, Y., Menon, V. 2025; 189: 256-274

    Abstract

    Previous studies exploring category-sensitive representations of numbers and letters have predominantly focused on individual brain regions. This study expands upon this research through computationally rigorous whole-brain neural decoding using Elastic Net (ND-EN), facilitating the analysis of neural patterns across the entire brain with greater precision. To establish the robustness and generalizability of our results, we also conducted innovative probabilistic meta-analyses of the extant functional neuroimaging literature. The investigation comprised both an active task, requiring participants to distinguish between numbers and letters, and a passive task where they simply viewed these symbols. ND-EN revealed that, during the active task, a distributed network-including the ventral temporal-occipital cortex, intraparietal sulcus, middle frontal gyrus, and insula-actively differentiated between numbers and letters. This distinction was not evident in the passive task, indicating that the task engagement level plays a crucial role in such neural differentiation. Further, regional neural representational similarity analyses within the ventral temporal-occipital cortex revealed similar activation patterns for numbers and letters, indicating a lack of differentiation in regions previously linked to these visual symbols. Thus, our findings indicate that category-sensitive representations of numbers and letters are not confined to isolated regions but involve a broader network of brain areas, and are modulated by task demands. Supporting these empirical findings, probabilistic meta-analyses conducted with NeuroLang and the Neurosynth database reinforced our observations. Together, the convergence of evidence from multivariate neural pattern analysis and meta-analysis advances our understanding of how numbers and letters are represented in the human brain.

    View details for DOI 10.1016/j.cortex.2025.04.017

    View details for PubMedID 40580696

  • Symbolic numerical generalization through representational alignment. CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference Strock, A., Liu, R., Iyer, R., Mistry, P. K., Menon, V. 2025; 47: 1882-1888

    Abstract

    The mapping between nonsymbolic quantities and symbolic numbers lays the foundation for mathematical development in children. However, the neural mechanisms underlying this crucial cognitive bridge remain unclear. Here, we investigate the computational principles governing symbolic-nonsymbolic integration using a biologically inspired neural network trained through developmentally inspired stages. Our investigation reveals that generalization from nonsymbolic to symbolic numerical processing emerges specifically when representational alignment forms between these numerical formats. Notably, this alignment appears to be stronger in cross-format comparison-based mapping compared to direct-label-based mapping. Furthermore, we demonstrate that subsequent symbolic specialization creates a representational divergence that impairs nonsymbolic performance while maintaining the ordinal structure of the mapping. These findings highlight representational alignment as a fundamental mechanism in numerical cognition and suggest that targeted cross-format comparison tasks may be particularly effective in improving mathematical learning in children with numerical processing difficulties.

    View details for PubMedID 40977751

  • Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically inspired neural network. Nature communications Mistry, P. K., Strock, A., Liu, R., Young, G., Menon, V. 2023; 14 (1): 3843

    Abstract

    Number sense, the ability to decipher quantity, forms the foundation for mathematical cognition. How number sense emerges with learning is, however, not known. Here we use a biologically-inspired neural architecture comprising cortical layers V1, V2, V3, and intraparietal sulcus (IPS) to investigate how neural representations change with numerosity training. Learning dramatically reorganized neuronal tuning properties at both the single unit and population levels, resulting in the emergence of sharply-tuned representations of numerosity in the IPS layer. Ablation analysis revealed that spontaneous number neurons observed prior to learning were not critical to formation of number representations post-learning. Crucially, multidimensional scaling of population responses revealed the emergence of absolute and relative magnitude representations of quantity, including mid-point anchoring. These learnt representations may underlie changes from logarithmic to cyclic and linear mental number lines that are characteristic of number sense development in humans. Our findings elucidate mechanisms by which learning builds novel representations supporting number sense.

    View details for DOI 10.1038/s41467-023-39548-5

    View details for PubMedID 37386013

    View details for PubMedCentralID 6506249