Julio E. Villalón‐Reina

This paper develops a lifespan normative model of brain white matter microstructure using diffusion MRI data from 54,583 individuals, enabling detection of microstructural deviations in diseases like Alzheimer's and 22q11.2 deletion syndrome.

SPECTRA improves white matter microstructure mapping by incorporating spatial resolution and adaptive error control in tractometry analysis.

BundleWarp improves white matter tractometry by aligning tracts while preserving anatomical features, enhancing sensitivity to disease-related changes in conditions like Alzheimer's and Parkinson's.

The paper compares deterministic and probabilistic tractography methods in ADNI data, finding bundle-level differences in white matter microstructure metrics between the two approaches.

This study evaluates how sample size and resolution affect tractometry's sensitivity and specificity in detecting dementia and amyloid-related effects in Alzheimer's disease.

This study uses diffusion MRI tractometry to identify localized white matter microstructural changes associated with stimulant misuse, finding distinct patterns for amphetamine use compared to cocaine use.

The paper examines how increasing training sample size improves the calibration of diffusion tensor imaging normative models using hierarchical Bayesian regression.

This study uses BUAN tractometry to assess how amyloid positivity affects white matter tracts in Alzheimer's disease, revealing localized microstructural changes associated with amyloid pathology.

This study compares deterministic and probabilistic tractography methods for the left arcuate fasciculus across four datasets, finding probabilistic methods yield higher inter-subject shape similarity but may obscure subtle pathological variations.

The paper creates lifespan reference curves for white matter microstructure using diffusion MRI data from multiple datasets and validates them with ApoE4 risk factor analysis.

The paper generates synthetic diffusion MRI maps using diffusion models and evaluates their realism and utility for improving AI classifiers.

This study evaluates 2D and 3D diffusion models for generating synthetic DTI maps, finding 3D models perform better in downstream tasks like sex and dementia classification.

This study introduces MINT, a framework that jointly models white matter microstructure and macrostructure to better detect anomalies in MCI and dementia, showing improved sensitivity compared to traditional diffusion metrics.

This study compares explainable AI models for Alzheimer's disease classification using MRI scans, evaluating interpretability methods and generalization across different populations.

This paper predicts white matter age using along-tract microstructural profiles from diffusion MRI to identify differential aging patterns and develop a new biomarker for neurodegenerative disease risk.

This study uses diffusion MRI and tractometry to analyze how amyloid, tau, and APOE genetic variants affect white matter microstructure in Alzheimer's disease.

This paper applies data harmonization techniques to 3D diffusion MRI tractometry to improve reliability and comparability across multiple scanning protocols.

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

BundleCleaner denoises and subsamples diffusion MRI tractography data, improving alignment with atlas bundles and enabling efficient tractometry analysis in Alzheimer's disease studies.

This study examines age and sex effects on white matter microstructure in 15,628 older adults using dMRI models, finding that advanced models like TDF and NODDI detect these effects most sensitively.

This study examines how exogenous sex hormones affect white matter aging in postmenopausal women using diffusion MRI data from the UK Biobank, finding steeper aging trajectories in hormone therapy users.

The paper evaluates tractography methods by analyzing a simulated dataset, finding that most algorithms generate many false-positive connections.