Alejandro I de la Vega

Functional decoding of brain activation maps has a new route: NiCLIP aligns neuroimaging article text with coordinate-derived activation maps in one shared latent space through contrastive learning, then uses a cognitive ontology to turn similarity into posterior probabilities over tasks, concepts and domains. Widely used correlation decoders and GC-LDA rely on precomputed meta-analytic maps and fixed vocabularies and give task-level predictions only. On Human Connectome Project group-level maps across seven domains, the best configuration reached 62.86% task recall against a 20.71% maximum for Neurosynth and GC-LDA, while the same model dropped markedly on subject-level maps.

Functional decoding of brain activation maps has a new route: NiCLIP aligns neuroimaging article text with coordinate-derived activation maps in one shared latent space through contrastive learning, then uses a cognitive ontology to turn similarity into posterior probabilities over tasks, concepts and domains. Widely used correlation decoders and GC-LDA rely on precomputed meta-analytic maps and fixed vocabularies and give task-level predictions only. On Human Connectome Project group-level maps across seven domains, the best configuration reached 62.86% task recall against a 20.71% maximum for Neurosynth and GC-LDA, while the same model dropped markedly on subject-level maps.