Bennett A. Landman

This paper evaluates the statistical validity of synthetic biomedical images and introduces PAMS to improve biological inference.

The paper generates lifespan normative charts for cervical spinal cord morphometry using MRI data, revealing non-linear changes and sex differences across ages 0-100.

The paper develops an automated pipeline for segmenting and parcellating brainstem and subcortical white matter pathways using diffusion MRI data.

This paper introduces a three-stage cascade pipeline for automated cerebral microbleed detection in SWI MRI, achieving high accuracy with reduced false positives.

This study identifies genetic loci and pathways associated with limbic white matter microstructure in aging and Alzheimer's Disease using multi-cohort diffusion MRI and bulk RNA-seq data.

This study characterizes white matter asymmetry across the lifespan using a large dataset, revealing dynamic changes in asymmetry patterns with age.

The paper uses machine learning to predict autopsy-confirmed neuropathology from clinical, neuroimaging, and CSF biomarkers, finding that different modalities best predict specific pathologies.

The paper develops normative reference charts for white matter microstructure and macrostructure across the lifespan using diffusion MRI data from 50 studies.

This paper introduces a method to generate white matter tractograms from FLAIR images without diffusion data, showing it captures structural probabilities similar to diffusion-based tractography.

The paper evaluates the variability of GAMLSS-based brain charts using analytic and empirical methods, finding low coefficient of variation across most white matter features.

This study maps short-range prefrontal connections in the human brain using tractography informed by primate histology, achieving high precision and accuracy.

This study uses multidimensional MRI to analyze cellular-scale microstructural changes in the human brain across aging, revealing increased heterogeneity and loss of microscopic restriction.

The paper describes improvements in diffusion MRI processing for the HBCD study, introducing new software and observing developmental patterns in brain data.

This study develops a novel mouse model of human lung cancer cachexia that exhibits early adipose tissue wasting and metabolic reprogramming, mirroring human disease features.

Radial diffusivity asymmetry driving DTI-ALPS indices pervades white matter, persists at high b-values, and tracks crossing fibers, axonal undulation and dispersion rather than perivascular diffusion.

This study compares the orientation of white matter nerve fibers and vasculature in the human brain using diffusion MRI and susceptibility weighted imaging, finding that vasculature aligns with some but not all white matter tracts.

This study develops a diffusion MRI protocol to simultaneously characterize brain and spinal cord microstructure using DTI, SMI, and SANDI, showing high image quality and reproducibility with some challenges in the spinal cord.

MidRISH harmonizes diffusion imaging data by projecting sites to a mid-space, reducing reference bias compared to LinearRISH.

The paper identifies a novel epithelial cell type, LND, in Crohn's disease that is associated with immune dysregulation and disease activity.

The paper examines how sex, race, and APOE-ε4 allele status affect white matter microstructure across multiple aging and Alzheimer's disease cohorts.

The paper identifies functional white matter networks in BOLD fMRI using hierarchical clustering and validates their stability across scans and populations.

This study evaluates the impact of distortion correction and cardiac triggering on spinal cord diffusion MRI quality, finding that distortion correction has limited effectiveness and cardiac triggering can be omitted without significant quality loss.

This paper evaluates the reliability and reproducibility of multidimensional diffusion-relaxation MRI (MD-MRI) in the human brain, showing it can reliably capture structural and chemical diversity within voxels.

The paper characterizes lifespan changes in white matter microstructure, macrostructure, and associated cortical morphology using large datasets, finding unique developmental and aging trajectories across brain pathways.

This paper uses deep learning to predict brain age from MRI data and finds it correlates with cognitive impairment and decline.

The paper investigates BOLD signal changes in both white and gray matter using model-free analysis and simple tasks, finding time-locked activations across the whole brain.

The paper analyzes longitudinal MRI data from 1,293 subjects to study age-related changes in superficial white matter microstructure and U-fiber volume and length.

This study quantifies superficial white matter volume and thickness across the lifespan using large datasets, finding unique age-related changes and relationships with cortical features.

The paper quantifies resting-state BOLD hemodynamic response functions (HRF) in white matter, showing significant differences from gray matter and variation along WM pathways.