Michael Zeineh

The paper uses diffusion MRI and SEEG to predict electrophysiological connectivity and explain evoked potential spectral signatures in the human brain.

This study identifies sex-specific changes in bone and senescence-related proteins in neurodegenerative diseases using plasma proteomics.

This paper introduces ComSLI, a method to map fiber networks in histology without sample preparation, showing its application across tissues and diseases.

This paper introduces a conditional diffusion model to denoise 7T MRI scans, improving image quality and outperforming existing methods.

This study uses a deep learning framework to analyze whole-genome data from ME/CFS patients, identifying risk genes and linking them to disease symptoms and other complex traits.

This paper develops a pipeline for precise MRI-histology coregistration using blockface imaging, achieving high alignment accuracy for neurodegenerative disease research.

The paper combines scattered light and X-ray imaging with diffusion MRI to accurately map neuronal orientations and validate fiber crossings in human and vervet monkey brains.

This paper demonstrates the use of small-angle X-ray scattering (SAXS) to detect myelinated axon crossings in multiple species, providing a ground truth for validating other imaging methods.

This study uses longitudinal diffusion MRI to examine microstructural changes in brain tracts of college football and volleyball players, finding divergent developmental patterns between the groups.

The paper examines dynamic anticorrelated activity between brain networks during attentional tasks and rest, finding time-lagged interactions linked to attentional performance.