Advanced Neuroimaging Techniques and Applications

This paper introduces DNT, a variational autoencoder framework for detecting white matter abnormalities in Alzheimer's disease using macro- and microstructural data.

This paper develops a method to construct morphological white matter networks from MRI data, showing they are reliable, heritable, and predictive of various brain features and diseases.

This study uses deep learning to estimate fiber orientation distribution functions (FODs) from limited diffusion MRI data in newborns and fetuses, achieving results comparable to standard methods with fewer measurements.

The paper uses diffusion tractography and machine learning to show that specific white matter tracts predict individual differences in sensorimotor learning outcomes.

The paper introduces MVComp, a tool for computing Mahalanobis distance in brain MRI data to assess deviations from reference groups and provide insights into white matter microstructure.

The paper quantifies structural connectivity between the hippocampus and reward regions in humans using diffusion MRI, finding region-specific connectivity patterns along the hippocampal axis.

This paper introduces an efficient in vivo MD-MRI protocol for whole-brain imaging and demonstrates its feasibility and robustness using phantoms and human scans.

The paper examines age-related changes in corpus callosum microstructure and how pulse pressure modifies these changes across different subregions.

The paper examines white matter microstructure differences between superagers and typical older adults over five years, finding slower age-related changes in superagers.

This study uses diffusion MRI to identify microstructural changes in the human brain associated with neurocognitive aging, finding that microstructural markers are more strongly linked to cognitive performance than macrostructural changes.

This study uses MRI metrics to identify microstructural differences in myelin and axons between ASD and neurotypical adolescents, finding increased extracellular water and decreased g-ratio and conduction velocity.

The paper examines how the timing of maternal ART initiation affects white matter integrity and organization in HIV-exposed uninfected neonates using DTI and graph theory.

This paper introduces a normative modelling approach to detect diffusion MRI artefacts and processing errors using UK Biobank data, outperforming traditional visual and quantitative QC methods.

GAN-MAT synthesizes T2-weighted MRI from T1-weighted data and estimates microstructural features, validated on healthy controls and patients with neurological conditions.

This study investigates how BMI-related variations in brain structure-function coupling change with diffusion time, revealing associations with hierarchical brain organization and gene expression in specific brain regions.

This paper introduces a personalized template mapping framework for diffusion MRI harmonization, improving performance over existing methods by preserving biological variability and reducing scanner-related differences.

The paper develops a mouse MRI atlas with both sexes included to address sex bias in preclinical research and demonstrates its utility in integrating imaging and molecular data.

The paper creates a macaque brain atlas using diffusion MRI data to map connectivity, parcellation, and histology, revealing structural differences between macaque and human brains.

The paper harmonizes diffusion MRI data from the ABCD study and provides white matter measures for structural connectivity research in adolescents.

The paper compares grey and white matter metrics in predicting cognitive performance in children using ABCD data, finding they provide distinct and complementary information.

This study examines individual variability and lateralization of the frontal aslant tract (FAT) in young adults using diffusion MRI, finding significant left lateralization and correlations with language function.

DCP automates diffusion tensor imaging processing on Windows to construct brain structure networks, offering a GUI and integration of existing tools.

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 study compares standard and axisymmetric DKI models, finding significant differences in biophysical parameter estimates, particularly for kurtosis and related metrics.

The paper introduces Pscore, a percentile-based metric to assess individual deviations in non-Gaussian diffusion MRI metrics, showing improved accuracy over Zscore in skewed distributions.

This study evaluates head motion correction methods in diffusion MRI, showing both Eddy and SHORELine perform well but are affected by denoising and sampling schemes.

The paper investigates how age-related changes in cortical microstructure depend on proximity to veins in human brain regions using venous distance mapping and MRI data.

ACID is an open-source MATLAB toolbox for processing and modeling diffusion MRI data from brain, spinal cord, and ex vivo specimens, incorporating artifact correction, biophysical models, and BIDS compatibility.

This paper uses high-resolution diffusion MRI to directly cluster cortical voxels and segment cytoarchitectonic domains without relying on templates, achieving better alignment with histology than conventional methods.

This study examines age-related changes in microstructure-informed cortical networks using diffusion MRI data from 8-18 year olds, finding increased global efficiency and mean strength in specific networks.