Biology subjects

Makin, S.

Publications and source records attributed to Makin, S..

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↗

bioRxiv2022

Improving the prediction of white matter hyperintensities evolution in brain MRI of patients with small vessel disease using stroke lesions information

Predicting the evolution of white matter hyperintensities (WMH), a common feature in brain magnetic resonance imaging (MRI) scans of older adults (i.e., whether WMH will grow, remain stable, or shrink with time) is important for personalised therapeutic interventions. However, this task is difficult mainly due to the myriad of vascular risk factors and comorbidities that influence it, and the low specificity and sensitivity of the image intensities and textures alone for predicting WMH evolution. Given the predominantly vascular nature of WMH, in this study, we evaluate the impact of incorporating stroke lesion information to a probabilistic deep learning model to predict the evolution of WMH 1-year after the baseline image acquisition, taken soon after a mild stroke event, using T2-FLAIR brain MRI. The Probabilistic U-Net was chosen for this study due to its capability of simulating and quantifying the uncertainties involved in the prediction of WMH evolution. We propose to use an additional loss called volume loss to train our model, and incorporate stroke lesions information, an influential factor in WMH evolution. Our experiments showed that jointly segmenting the disease evolution map (DEM) of WMH and stroke lesions, improved the accuracy of the DEM representing WMH evolution. The combination of introducing the volume loss and joint segmentation of DEM of WMH and stroke lesions outperformed other model configurations with mean volumetric absolute error of 0.0092 ml (down from 1.7739 ml) and 0.47% improvement on average Dice similarity coefficient in shrinking, growing and stable WMH.

bioinformatics↗

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

Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map

We propose a Generative Adversarial Network (GAN) model named Disease Evolution Predictor GAN (DEP-GAN) to predict the evolution (i.e., progression and regression) of White Matter Hyperintensities (WMH) in small vessel disease. In this study, the evolution of WMH is represented by the \"Disease Evolution Map\" (DEM) produced by subtracting irregularity map (IM) images from two time points: baseline and follow-up. DEP-GAN uses two discriminators (critics) to enforce anatomically realistic follow-up image and DEM. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we propose modulating an array of random noises to the DEP-GANs generator which forces the model to imitate a wider spectrum of alternatives in the results. Our study shows that the use of two critics and random noises modulation in the proposed DEP-GAN improves its performance predicting the evolution of WMH in small vessel disease. DEP-GAN is able to estimate WMH volume in the follow-up year with mean (std) estimation error of -1.91 (12.12) ml and predict WMH evolution with mean rate of 72.01% accuracy (i.e., 88.69% and 23.92% better than Wasserstein GAN).

neuroscience↗