R. Austin Benn

The paper identifies a topographic organization of mental imagery in the human parietal cortex, showing modality-specific regions and a central hub for multisensory imagery.

This paper compares neocortical sulcal anatomy across nine pinniped species to understand how cortical organization changes with adaptation to marine environments.

This study shows early sensory deprivation causes localized reorganization near deprived primary sensory areas while preserving the overall cortical hierarchy.

The paper tests if cortical geometry encodes sensory function by modeling multisensory integration from geometric eigenmodes, finding they align with sensory domains but require additional modes for full hierarchy.

The paper introduces SCD to analyze spatial influences on cortical functional specialization, revealing a dominant gradient and competitive suppression from other brain regions.

Precon_all is a pipeline for non-human cortical surface reconstruction that generalizes human methods to multiple species, enabling comparative neuroimaging studies.

The paper uses predictive modeling to show that stronger oral reading ability correlates with closer proximity between visual and association cortex in the brain.

The paper predicts individual brain activity patterns during tasks using resting-state networks, showing task-specificity and relevance of cortical microstructure/morphology.

This study examines neocortical sulcal anatomy across 26 carnivoran species using neuroimaging to identify patterns related to behavior and ecology.

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 uses variograms to show that function changes more rapidly in association cortex than in sensory and motor cortex, linked to intracortical myelin levels.

The paper introduces PRIME-RE, a collaborative platform for sharing NHP neuroimaging resources, tools, and data.

The paper develops a transfer-learning U-Net model for brain extraction in non-human primates using human data, achieving better performance than traditional methods and popular MRI packages.

This paper creates a common space for comparing pig and human structural connectomes to assess the translational validity of porcine neurological disease models.