Brain-wide decoding of numbers and letters: Converging evidence from multivariate fMRI analysis and probabilistic meta-analysis

Ruizhe Liu; Hyesang Chang; Dawlat El-Said; Demian Wassermann; Yuan Zhang; Vinod Menon

doi:10.1016/j.cortex.2025.04.017

Brain-wide decoding of numbers and letters: Converging evidence from multivariate fMRI analysis and probabilistic meta-analysis

Cortex. 2025 Jun 13:189:256-274. doi: 10.1016/j.cortex.2025.04.017. Online ahead of print.

Authors

Ruizhe Liu¹, Hyesang Chang², Dawlat El-Said², Demian Wassermann³, Yuan Zhang², Vinod Menon⁴

Affiliations

¹ Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA. Electronic address: rul23@stanford.edu.
² Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.
³ Inria Saclay Île-de-France, CEA, Université Paris-Saclay, Palaiseau, France.
⁴ Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA; Graduate School of Education, Stanford University, Stanford, CA, USA. Electronic address: menon@stanford.edu.

PMID: 40580696
DOI: 10.1016/j.cortex.2025.04.017

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.

Keywords: Distributed neural representation; Multivariate decoding; Neural representational similarity; Quantitative meta-analysis; Ventral temporal-occipital cortex.