Demián Wassermann

The paper decodes language state in bilinguals using fMRI and identifies shared and individual brain organization patterns.

This paper introduces MDSI-AVI, a framework for modeling whole-brain causal interactions in fMRI data, revealing new insights into working memory mechanisms.

NeuroConText improves brain meta-analysis by using contrastive learning and large language models to better retrieve and reconstruct brain activation maps from neuroscience texts.

This study compares machine learning models using functional connectivity and its derivatives to predict schizophrenia, finding that raw connectivity outperforms low-dimensional features.

The paper introduces NeuroLang, a probabilistic logic programming language for neuroimaging meta-analysis, demonstrating its ability to infer functional brain network roles and address complex hypotheses.

This study uses a novel meta-analysis approach to identify functional gradients in the human lateral prefrontal cortex from 14,371 neuroimaging publications.

The paper introduces a language and tool for defining and reconstructing white matter tracts from dMRI data, showing alignment with neuroanatomical literature and applications in functional subdivisions and lateralization studies.

The paper uses diffusion MRI to analyze microstructural and macrofunctional organization of the human and macaque insula, linking these features to cognitive control abilities.