Michele Svanera

This paper identifies systematic biases in ADNI neuroimaging data that affect machine learning predictive modeling and proposes a framework to address these issues.

This paper uses 7-Tesla MRI to identify distinct T1-weighted intensity profiles within the human insula cortex, revealing compartment-specific signal variations and highlighting atlas-dependent parcellation.

CEREBRUM-7T is a fast, fully-volumetric brain segmentation method for 7T MRI that outperforms its training labels and is suitable for neuroimaging studies.

This paper compares a self-supervised CNN for image completion with fMRI data from early visual cortex, finding the CNN's decoder pathway more similar to brain activity than a supervised model.

This paper transfers deep neural network features to fMRI data to improve decoding performance by reconstructing CNN features from imaging data.