Dinggang G. Shen

This study uses diffusion MRI to show that speech acquisition in infants involves coordinated white-matter maturation across multiple systems, not just language pathways.

This paper develops BrainMLSR, an automated framework to reconstruct three intracortical signal-defined layers from 5T 3D FLAIR MRI, showing high agreement with manual annotations and revealing regional morphometric differences in epilepsy.

This study introduces NAT, a tool for analyzing auditory tract maturation in infants using diffusion MRI, and links it to speech production and motor skills.

The paper introduces BEN, a deep learning method for brain tissue extraction across species and MRI modalities, showing improved accuracy and generalizability over existing tools.

This paper introduces 3D-MASNet, a 3D mixed-scale asymmetric convolutional network for segmenting infant brain MR images, achieving high Dice coefficients in GM, WM, and CSF.

This study investigates how functional connectivity dynamics develop in the human brain during the first two years of life using longitudinal fMRI data from infants.

This paper introduces an interpretable deep learning model to detect Alzheimer's disease by analyzing longitudinal neuroimaging data from the ADNI cohort.

This paper proposes a transfer learning method to improve functional brain network estimation for MCI identification, achieving higher classification accuracy and AUC.

This paper proposes a method for estimating functional brain networks by scrubbing noisy time series data, achieving higher classification accuracy in identifying MCI patients.