Leila Wehbe

The paper uses hypothesis-neutral models to show that higher visual areas exhibit category selectivity without explicit training on those categories.

The paper validates a diffusion-based method called BrainDiVE for generating stimuli that activate specific brain regions, showing enhanced category selectivity and revealing functional gradients in visual cortex.

The paper identifies two cortical mechanisms for audiovisual processing during naturalistic stimuli using fMRI data and prediction-based approaches.

This paper develops a low-rank tensor regression method to decompose neural responses to language features in MEG data, showing improved encoding performance and interpretable components.

The paper trains an autoregressive Transformer on neural spiking data to decode motor behavior, showing broad utility across tasks but highlighting limitations in scaling.

This paper introduces stacking and structured variance partitioning methods to improve interpretability of brain maps by handling correlated stimulus features.

The paper uses a texture statistics model to predict fMRI responses across human visual cortex, showing increasing complexity of texture features from early to higher visual areas.

The paper shows that low-level visual feature tuning in visual cortex correlates with semantic informativeness, supporting the natural scene statistics hypothesis.

This study shows that context significantly affects the quality of neuroimaging data and the representation of semantic information in the brain during language comprehension.

This study investigates cortical selectivity to real-world object size in natural images, finding consistent organization across occipitotemporal cortex and preserved spatial patterns despite controlling for visual features.

The paper identifies two food-selective regions in the ventral visual cortex using fMRI and natural images, confirming food selectivity across multiple datasets and analysis methods.

This paper uses explainable AI to analyze brain responses to natural images, revealing distinct spatial feature selectivity in different visual regions.

NDT2 pretrains a spatiotemporal Transformer on multi-session motor BCI data to improve decoding performance across novel contexts.

The paper investigates how the brain processes combined meaning of words beyond individual meanings using computational methods and brain imaging.

The paper shows that using natural language supervision with large, diverse datasets improves models' ability to predict human high-level visual cortex responses compared to image-only or text-only models.

This paper introduces brainprints, interpretable MEG features that can identify individuals from anonymized MEG data across sessions and modalities.

The paper examines the relationship between comprehension difficulty and neural activity in the multiple demand and core language networks during story listening, finding no link in the multiple demand network but a link in the core language network.

This study investigates whether fMRI can reveal brain representations of syntactic structure by modeling brain activity using syntactic features from natural text.

The paper uses computer vision tasks to predict brain activity and maps task-specific visual features to brain regions, revealing task-specific architecture in the human visual system.

This study uses MEG to track how the brain processes adjective-noun phrases, finding that adjective representations change during noun processing and are reactivated later.