Biology subjects

Valdes Hernandez, M. d. C.

Publications and source records attributed to Valdes Hernandez, M. d. C..

4 recordsLinked to original sources

bioRxiv2026

APOE4 genotype, blood-brain barrier leakage and ischaemic stroke subtype and location

BackgroundApolipoprotein E (APOE) has been implicated in blood-brain barrier (BBB) dysfunction and may influence ischaemic cerebrovascular disease and cerebral small-vessel disease (cSVD). This study examined associations between APOE genotype, BBB permeability, and infarct distribution in patients with mild ischemic stroke. MethodsWe recruited patients with mild ischemic stroke who underwent structural and dynamic contrast-enhanced MRI (DCE-MRI) and APOE genotyping. Infarct type and location, white matter hyperintensities (WMH), and perivascular spaces (PVS) were assessed. BBB-related metrics were quantified using fractional plasma volume (vP) and permeability-surface area product (PS) across five brain regions of interest: deep grey matter (DGM), hippocampus, thalamus, normal-appearing white matter (NAWM), and WMH. Associations between genotype, BBB metrics, vascular risk factors, and age were evaluated using linear mixed-effects models. Binary logistic regression was also applied to assess the association between APOE status and infarct location by vascular territory (anterior vs posterior circulation). ResultsAmong 147 patients with APOE genotype and BBB measures, APOE4 carriers (n=44) demonstrated a greater proportion of posterior circulation infarcts than E3/E3 individuals (n=80; 56.4% versus 32.9%), including higher frequencies of posterior cerebral artery cortical, posterior borderzone, and thalamic infarcts. Mean PS and vP did not differ significantly by genotype. Increasing age was associated with lower PS across multiple regions and lower vP in WMH, while higher vascular risk burden was associated with lower vP in NAWM and WMH. Inclusion of regional BBB metrics did not substantially alter APOE4 effect estimates in infarct-location models. ConclusionsAPOE4 carriers showed a posterior-predominant infarct distribution despite similar BBB permeability and vascularity measures. Age and vascular risk burden were more strongly associated with BBB-related imaging metrics than APOE genotype. These findings add to evidence suggesting that APOE genotype may influence regional cerebrovascular vulnerability and that this effect is unlikely to be fully explained by DCE-MRI-derived measures of BBB permeability and vascularity alone.

neuroscience↗

bioRxiv2020

Three major dimensions of human brain cortical ageing in relation to cognitive decline across the 8th decade of life.

Different brain regions can be grouped together, based on cross-sectional correlations among their cortical characteristics; this patterning has been used to make inferences about ageing processes. However, cross-sectional brain data conflates information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the 8th decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1,376 MRI scans. Volumetric changes among cortical regions of interest (ROIs) were more strongly correlated (average r = 0.805, SD = 0.252) than were cross-sectional volumes of the same ROIs (average r = 0.350, SD = 0.178). We identified a broad, cortex-wide, dimension of atrophy that explained 66% of the variance in longitudinal changes across the cortex. Our modelling also discovered more specific fronto-temporal and occipito-parietal dimensions, that were orthogonal to the general factor and together explained an additional 20% of the variance. The general factor was associated with declines in general cognitive ability (r = 0.431, p < 0.001) and in the domains of visuospatial ability (r = 0.415, p = 0.002), processing speed (r = 0.383, p < 0.001) and memory (r = 0.372, p < 0.001). Individual differences in brain cortical atrophy with ageing are manifest across three broad dimensions of the cerebral cortex, the most general of which is linked with cognitive declines across domains. Longitudinal approaches are invaluable for distinguishing lifelong patterns of brain-behaviour associations from patterns that are specific to aging.

neuroscience↗

bioRxiv2019

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the models architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.

neuroscience↗

bioRxiv2019

Dilated Saliency U-Net for White Matter Hyperintensities Segmentation using Irregularity Age Map

White matter hyperintensities(WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image(MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for Alzheimer s disease and brain aging processes. However, the automatic WMH segmentation is challenging because of the variable intensity range, size and shape. U-Net tackled this problem through the dense prediction and showed competitive performances on not only WMH segmentation/detection but also on varied image segmentation tasks, but it still accompanies a high complexity of the network architecture. In this study, we propose to use Saliency U-Net architecture and irregularity age map(IAM) to decrease the U-Net complexity without a performance loss. We trained Saliency U-Net using both T2-FLAIR MRI sequence and IAM. Since IAM guides where irregularities, in which WMH is possibly included, exist on the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The better performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net to recognise the shape of large WMH more accurately by learning multi-context on MRI slices. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which is the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest train time and the least number of parameters. In conclusion, based on the experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation.

neuroscience↗