Marieke Mur

This study shows that individualized parcellation of the human prefrontal cortex reveals sharp functional boundaries not visible in group atlases, suggesting a mosaic of fine-grained subdivisions within large-scale gradients.

This study uses frequency-tagged fMRI to show that visual cortical responses are suppressed during inattention and enhanced during attention, supporting the normalization model of attention.

The paper shows that stimulus effects dominate representational variance in visual cortex, while task effects dominate in prefrontal and parietal cortex, suggesting distinct optimization goals for sensory vs. higher-order brain regions.

The paper introduces an open-source Python toolbox for representational similarity analysis, providing tools to evaluate neural representations and models.

This study uses frequency-tagging fMRI to show that directed attention enhances anti-phase BOLD oscillations and suppresses in-phase oscillations in visual cortex, supporting the biased competition model of attention.

This paper develops and validates a dual-frequency tagging fMRI protocol to enable fine-grained spatiotemporal analysis of cortical function, revealing detailed cortical topographies and temporal synchronization.

This study examines how neural activity in the primate lateral prefrontal cortex is organized during different cognitive tasks, finding that task-related activity has a consistent spatial scaffold with task-specific reconfiguration.

The paper shows that visuo-semantic features explain more variance in higher-level ventral stream neural dynamics than deep neural networks, suggesting complementary roles in object recognition.

This paper introduces metrics to analyze linear transformations between multivariate fMRI patterns in different brain regions, showing they explain significant variance in output regions.

The paper shows that the human occipital place area (OPA) encodes scene layout invariant to surface texture within 100 ms, contrasting with V1 and PPA.

The paper compares categorical and feature-based models in explaining IT cortex representations and similarity judgments, finding that shared variance between features and categories best explains IT representations, while similarity judgments are better explained by categories alone.

The paper tests the faciotopy hypothesis, finding that the OFA shows evidence of a face-feature map with cortical distances reflecting physical feature distances in faces.