Inter-individual and inter-site neural code conversion without shared stimuli.

ABSTRACT: Inter-individual variability in fine-grained functional topographies poses challenges for scalable data analysis and modeling. Functional alignment techniques can help mitigate these individual differences but they typically require paired brain data with the same stimuli between individuals, which are often unavailable. Here we present a neural code conversion method that overcomes this constraint by optimizing conversion parameters based on the discrepancy between the stimulus contents represented by original and converted brain activity patterns. This approach, combined with hierarchical features of deep neural networks as latent content representations, achieves conversion accuracies that are comparable with methods using shared stimuli. The converted brain activity from a source subje

SUBMITTER: Wang H 

PROVIDER: S-EPMC12286860 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature