Maria del Carmen Valdés Hernández

This study examines the association between APOE4 genotype, BBB permeability, and infarct distribution in patients with mild ischemic stroke, finding a posterior-predominant infarct distribution in APOE4 carriers despite similar BBB metrics.

This paper evaluates methods to estimate lifetime brain atrophy from a single MRI scan and identifies genetic correlates, finding that the residual method best captures brain shrinkage and has strong heritability.

This paper predicts one-year WMH evolution post-stroke using MRI and stroke lesion data with a probabilistic deep learning model, achieving improved accuracy through joint segmentation and a new loss function.

The paper identifies brain regions most related to general cognitive function and links them to neurobiological properties using meta-analysis and multimodal cortical maps.

The paper develops epigenetic scores for GDF15 and NT-proBNP levels, showing associations with disease risks and brain health traits in two cohorts.

The paper identifies general and specific patterns of cortical gene expression related to cognitive functioning using principal component analysis and validates them across datasets.

This paper develops an epigenetic score to objectively measure alcohol consumption using blood-based DNA methylation and validates it across multiple cohorts, finding associations with brain health metrics.

This study identifies DNA methylation predictors of cognitive function using blood samples, showing they explain variance in cognitive ability and associate with biological markers.

The paper examines the association between birth weight and brain structure in older age, finding positive links with brain volumes but not with age-related structural changes.

This study identifies lipid biomarkers associated with brain structure but not cognitive ability in older adults using metabolomic data from the LBC1936 cohort.

This paper introduces DEP models to predict white matter hyperintensity evolution in brain MRI, showing that semi-supervised models with Gaussian noise inputs perform best.

This study examines the genomic architecture of neuroanatomical diversity using SNP data and MRI, finding polygenic influences and environmental factors affecting brain asymmetry.

The paper quantifies perivascular spaces (PVS) in brain MRI and links them to retinal microvascular parameters in older adults, finding associations with arteriolar narrowing and reduced branching complexity.

The paper examines how aging affects brain network integrity and its association with cognitive function using MRI data from two cohorts.

This paper introduces DNAm GrimAge, an epigenetic clock that predicts mortality and is associated with multiple brain health measures in older adults.

The paper introduces LOTS-IM, an unsupervised method for detecting white matter hyperintensities and MS lesions in MRI, showing better performance than existing methods.

The paper quantifies microstructural changes in normal-appearing white matter near white matter hyperintensities in older adults, showing spatial gradients of damage linked to WMH burden.

This study identifies neurology-related proteins in plasma associated with cognitive ability and brain volume in older adults, with partial mediation by brain volume.

This paper introduces a Dilated Saliency U-Net for WMH segmentation using an irregularity age map, achieving better performance with fewer parameters and shorter training time.

The paper evaluates enhanced machine learning techniques for segmenting ischaemic stroke lesions in MR perfusion images using a CNN, finding data augmentation with binary closing improves segmentation accuracy.

The paper examines associations between retinal vascular parameters and cerebral small vessel disease in two cohorts, finding that arteriolar fractal dimension relates to white matter hyperintensities.

This paper assesses the hierarchical complexity of human structural connectomes, finding that middle-tier nodes exhibit the highest complexity and align with known functional brain organization.

This paper introduces DEP-GAN, a GAN model that predicts WMH evolution in small vessel disease using irregularity maps and two discriminators, achieving 72.01% accuracy in predicting WMH evolution.

This study uses an Irregularity Age Map to adapt transfer learning for brain lesion segmentation and predict WMH progression/regression without manual labels.

The paper matches 73- and 92-year-olds for childhood intelligence, social background, and brain size, finding significant structural brain differences linked to cognitive decline.

The paper evaluates 15 candidate SNPs associated with psychiatric disorders using 6,165 diffusion MRI scans, finding no significant associations after correction for multiple testing.