Biology subjects

Black, S. E.

Publications and source records attributed to Black, S. E..

10 recordsLinked to original sources

bioRxiv2026

White Matter Lesion Asymmetry and Carotid Intraplaque Hemorrhage: A Novel Imaging Phenotype for Stroke Risk in Older Men

BackgroundWhile identifying high-risk carotid disease remains a significant clinical challenge, the specific role of carotid intraplaque hemorrhage (IPH) is poorly understood. Although IPH has been linked to white matter lesion (WML) burden, current assessments overlook the directional impact of plaque instability on the brain. This study sought to determine whether IPH is an independent driver of asymmetric WML pathology and evaluate if this phenotype can identify a high-risk demographic for TIA and stroke. MethodsThis multi-center retrospective study analyzed 264 participants (mean age 71.8 years) from the Canadian Atherosclerosis Imaging Network (2010-2015). Participants underwent 3T MRI to assess carotid IPH and WMLs. We quantified WMLs using a deep-learning pipeline to extract three biomarkers: volume (WML-ICV), intensity (WML-Intensity), and intensity ratio (WML-IR). The Asymmetry Index Measure (AIM) defined the inter-hemispheric log-ratio, while the association between IPH and AIM was examined using multivariable linear regression adjusted for age, sex, stenosis, and scanner manufacturer. A secondary composite outcome of TIA/stroke was analyzed via logistic regression to evaluate the interaction between IPH, age, and sex. ResultsWhile whole-brain WML burden did not significantly differ by IPH status (p > 0.60), IPH status was a robust independent predictor of hemispheric asymmetry (WML-ICV: p = 0.01; WML-Intensity, p = 0.01). Post-hoc analysis confirmed WML burden was significantly higher in IPH+ older males ([≥] 70 years) compared to younger cohorts (p < 0.04). This older male subgroup also demonstrated 4.57-fold higher adjusted odds of TIA/stroke (p = 0.02) compared to other demographic subgroups (all p > 0.87). ConclusionsCarotid IPH is independently associated with a rightward asymmetric WML phenotype not captured by global metrics. This imaging marker identifies a high-risk demographic of older males with a nearly five-fold increase in clinical events, suggesting that hemispheric-level analysis provides critical prognostic value for stroke risk stratification.

neuroscience↗

bioRxiv2024

NEUROG2 regulates a human-specific neurodevelopmental gene regulatory program

Unique hallmarks of human neocortical development include slower rates of neurogenesis and the establishment of an extracellular matrix-rich, outer-subventricular zone that supports basal neural progenitor cell expansion. How gene regulatory networks have evolved to support these human-specific neurodevelopmental features is poorly understood. Mining single cell data from cerebral organoids and human fetal cortices, we found that NEUROG2 expression is enriched in basal neural progenitor cells. To identify and purify NEUROG2-expressing cells and trace their short-term lineage, we engineered two NEUROG2-mCherry knock-in human embryonic stem cell lines to produce cerebral organoids. Transcriptomic profiling of mCherry-high organoid cells revealed elevated expression of PPP1R17, associated with a fast-evolving human-accelerated regulatory region, oligodendrocyte precursor cell and extracellular matrix-associated gene transcripts. Conversely, only neurogenic gene transcripts were enriched in mCherry-high cortical cells from Neurog2:mCherry knock-in mice. Finally, we show that Neurog2 is sufficient to induce Ppp1r17, which slows human neural progenitor cell division, and Col13a1, an extracellular matrix gene, in P19 cells. NEUROG2 thus regulates a human neurodevelopmental gene regulatory program implicated in supporting a pro-proliferative basal progenitor cell niche and tempering the neurogenic pace. SUMMARY STATEMENTTranscriptomic analyses of NEUROG2-mCherry knock-in human embryonic stem cell-derived cerebral organoids reveal a link between NEUROG2 and extracellular matrix remodeling during human cortical development.

developmental biology↗

bioRxiv2021

Lower Aβ-PET signal in white matter lesions relates to higher extracellular free water in mixed small vessel disease and Alzheimer's pathology

White matter (WM) injury is frequently observed along with dementia. Positron emission tomography with amyloid-ligands (A{beta}-PET) recently gained interest for detecting WM injury. Yet, little is understood about the origin of the altered A{beta}-PET signal in WM regions. Here, we investigated the relative contributions of diffusion MRI-based microstructural alterations, including free water and tissue-specific properties, to A{beta}-PET in WM and to cognition. We included a unique cohort of 115 participants covering the spectrum of low-to-severe white matter hyperintensity (WMH) burden and cognitively normal to dementia. We applied a bi-tensor diffusion-MRI model that differentiates between (i) the extracellular WM compartment (represented via free water), and (ii) the fiber-specific compartment (via free water-adjusted fractional anisotropy [FA]). We observed that, in regions of WMH, a decrease in A{beta}-PET related most closely to higher free water and higher WMH volume. In contrast, in normal-appearing WM, an increase in A{beta}-PET related more closely to higher cortical A{beta} (together with lower free water-adjusted FA). In relation to cognitive impairment, we observed a closer relationship with higher free water than with either free water-adjusted FA or WM PET. Our findings support free water and A{beta}-PET as markers of WM abnormalities in patients with mixed dementia, and contribute to a better understanding of processes giving rise to the WM PET signal.

neuroscience↗

bioRxiv2021

Increased Brain Volumetric Measurement Precision from Multi-Site 3D T1-weighted 3T Magnetic Resonance Imaging by Correcting Geometric Distortions

Magnetic resonance imaging (MRI) scanner-specific geometric distortions may contribute to scanner induced variability and decrease volumetric measurement precision for multi-site studies. The purpose of this study was to determine whether geometric distortion correction increases the precision of brain volumetric measurements in a multi-site multi-scanner study. Geometric distortion variation was quantified over a one-year period at 10 sites using the distortion fields estimated from monthly 3D T1-weighted MRI geometrical phantom scans. The variability of volume and distance measurements were quantified using synthetic volumes and a standard quantitative MRI (qMRI) phantom. The effects of geometric distortion corrections on MRI derived volumetric measurements of the human brain were assessed in two subjects scanned on each of the 10 MRI scanners and in 150 subjects with cerebrovascaular disease (CVD) acquired across imaging sites. Geometric distortions were found to vary substantially between different MRI scanners but were relatively stable on each scanner over a one-year interval. Geometric distortions varied spatially, increasing in severity with distance from the magnet isocenter. In measurements made with the qMRI phantom, the geometric distortion correction decreased the standard deviation of volumetric assessments by 35% and distance measurements by 42%. The average coefficient of variance decreased by 16% in gray matter and white matter volume estimates in the two subjects scanned on the 10 MRI scanners. Geometric distortion correction using an up-to-date correction field is recommended to increase precision in volumetric measurements made from MRI images.

bioengineering↗

bioRxiv2021

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation

White matter hyperintensities (WMH) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D Convolutional Neural Network (CNN) with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. 432 subjects were recruited to train the CNNs from four multi-site imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multi-site study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 {+/-} 0.08 and the lowest modified Hausdorff distance of 2.98 {+/-} 4.40 mm. We further validated our models highlighting their robustness on clinical adversarial cases simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io

neuroscience↗

bioRxiv2021

Automated Generation of Cerebral Blood Flow Maps Using Deep Learning and Multiple Delay Arterial Spin-Labelled MRI

The purpose of this work was to develop and evaluate a deep learning approach for estimation of cerebral blood flow (CBF) and arterial transit time (ATT) from multiple post-label delay (PLD) arterial spin-labelled (ASL) MRI. Six-PLD ASL MRI was acquired on a 1.5T or 3T system among 99 older males and females with and without cognitive impairment. We trained and compared two network architectures: standard feed-forward convolutional neural network (CNN) and U-Net. Mean absolute error (MAE) was evaluated between model estimates and ground truth obtained through conventional processing. The best-performing model was re-trained on inputs with missing PLDs to investigate generalizability to different PLD schedules. Relative to the CNN, the U-Net yielded lower MAE on training data. On test data, the U-Net MAE was 8.4{+/-}1.4 ml/100g/min for CBF and 0.22{+/-}0.09 s for ATT. Model uncertainty, estimated with Monte Carlo dropout, was associated with model error. Network estimates remained stable when tested on inputs with up to three missing PLD images. Mean processing times were: U-Net pipeline = 10.77s; ground truth pipeline = 10min 41s. These results suggest hemodynamic parameter estimation from 1.5T and 3T multi-PLD ASL MRI is feasible and fast with a deep learning image-generation approach.

bioengineering↗

bioRxiv2020

Visual QC Protocol for FreeSurfer Cortical Parcellations from Anatomical MRI

Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy-to-use open source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets, and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.

neuroscience↗

bioRxiv2020

Cortical thickness estimation in individuals with cerebral small vessel disease, focal atrophy, and chronic stroke lesions.

BackgroundRegional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases can be estimated using specialised neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures. PurposeThe main goal of this study was to examine a correction procedure developed for enhancing FreeSurfers cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and cerebrovascular disease, with varying degrees of neurovascular lesions and brain atrophy. MethodsIn 155 cerebrovascular disease patients enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FreeSurfer outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures. ResultsCorrected procedures increased Acceptable QC ratings from 18% to 76% for the cortical ribbon and from 38% to 92% for tissue segmentation. Corrected procedures reduced Fail ratings from 11% to 0% for the cortical ribbon and 62% to 8% for tissue segmentation. FreeSurfer-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8mL vs. 15.9mL, p<0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p=0.018) and left insula (p=0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p<0.001). ConclusionsThese findings suggest that correction procedures that account for brain atrophy and neurovascular lesions can significantly improve FreeSurfers segmentation results, reduce failure rates, and potentially increase sensitivity to examine brain-behaviour relationships. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and neurodegenerative disease in the ONDRI study.

neuroscience↗

bioRxiv2020

An overview of the quality assurance and quality control of magnetic resonance imaging data for the Ontario Neurodegenerative Disease Research Initiative (ONDRI): pipeline development and neuroinformatics

Large scale research studies combining magnetic resonance imaging data generated at multiple sites on multiple vendor platforms are becoming more commonplace. The Ontario Neurodegenerative Disease Research Initiative (ONDRI - http://ondri.ca/), a project funded by the Ontario Brain Institute (OBI), is a recently established province-wide natural history study, which has recruited more than 500 participants from neurodegenerative disease groups including amyotrophic lateral sclerosis, fronto-temporal dementia, Parkinsons disease, Alzheimers disease, mild cognitive impairment, and cerebrovascular disease (previously referred to as the vascular cognitive impairment cohort). Because of its multi-site nature, all captured data must be standardized and meet minimum quality standards to reduce variability. The goal of the ONDRI imaging platform is to maximize data quality by implementing vendor-specific harmonized MR imaging protocols (consistent with the Canadi-an Dementia Imaging Protocol - http://www.cdip-pcid.ca/), monitoring protocol adherence, qualitatively assessing image quality, measuring signal-to-noise and contrast-to-noise, monitoring system stability, and applying corrections based on the analysis of images from two different phantoms regularly acquired at each site. To maximize image quality, this work describes the use of various automatic pipelines and manual assessment steps, integrated within an established informatics and databasing platform, the Stroke Patient Recovery Research Database (SPReD) built on the Extensible Neuroimaging Archive Toolkit (XNAT), and contained within the Brain-CODE (Centre for Ontario Data Exploration) framework. The purpose of the current paper is to describe the steps undertaken by ONDRI to achieve this high standard of data integrity. Data have been successfully collected for the past 4 years with the pipelines and assessments identifying deviations, allowing for timely interventions and assessment of image quality.

neuroscience↗

bioRxiv2019

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI methods & outcome measures

The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 year multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimers disease, mild cognitive impairment, Parkinsons disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease patients. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. This paper describes the comprehensive neuroimaging pipeline methods used to generate regional brain tissue volumes & neurovascular markers.

neuroscience↗