Robustness of Probabilistic U-Net for Automated Segmentation of White Matter Hyperintensities in Different Datasets of Brain MRI

2021 ◽  
Author(s):  
Rizal Maulana ◽  
Muhammad Febrian Rachmadi ◽  
Laksmita Rahadianti
Keyword(s):  
White Matter ◽  
White Matter Hyperintensities ◽  
Brain Mri ◽  
Automated Segmentation

Abstract P366: High Burden of Tract-Specific White Matter Hyperintensities Relates to Impaired Microstructural Integrity of Distal White Matter Tracts in Community-Dwelling Elders

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
YANPENG LIU ◽  
YIWEI XIA ◽  
XIAOXIAO WANG ◽  
YI WANG ◽  
LUMENG YANG ◽  
...  
Keyword(s):  
Executive Function ◽  
White Matter ◽  
Cognitive Dysfunction ◽  
White Matter Hyperintensities ◽  
Brain Mri ◽  
Mean Diffusivity ◽  
Community Dwelling ◽  
Superior Longitudinal Fasciculus ◽  
High Burden ◽  
Microstructural Integrity

Background and purpose: White matter hyperintensities (WMH) are common in elderly individuals and contribute to age-related cognitive dysfunction. Converging evidence indicates that WMH affect white matter (WM) microstructural integrity in WMH and their penumbra. We aimed to investigate whether this effect extends to the distal WM tracts, and to examine the association between distal WM microstructural integrity and cognitive dysfunction in community-dwelling elderly people. Methods: Brain MRI data including FLAIR and DTI sequences of 174 participants (74 ± 5 years) of the Shanghai Aging Study (SAS) were collected and analyzed. For each participant, WMH lesions were segmented automatically. Eighteen major WM tracts were reconstructed using automated quantitative tractography, and the mean diffusivity (MD) of distal WM tracts (excluding an area of 12 mm around the WMH) was calculated. Multivariable linear regression was performed. Results: A high burden of tract-specific WMH was related to a high MD of distal WM tracts in the forceps major (FMA), anterior thalamic radiations (ATR), cingulum cingulate gyrus (CCG), corticospinal tract (CST), superior longitudinal fasciculus-parietal (SLFP), superior longitudinal fasciculus-temporal (SLFT), and uncinate fasciculus (UNC). Furthermore, a high MD of distal tracts was linked to worse attention and executive function in the forceps minor (FMI), right CCG, left inferior longitudinal fasciculus (ILF), SLFP, SLFT and UNC. Conclusions: The effect of WMH on the microstructural integrity of WM tracts may propagate along tracts to distal regions farther than the penumbra and eventually might affect attention and executive function.

Slow-wave Sleep and MRI Markers of Brain Aging in a Community-based Sample

Neurology ◽  
2020 ◽  
pp. 10.1212/WNL.0000000000011377
Author(s):  
Andree-Ann Baril ◽  
Alexa S Beiser ◽  
Vincent Mysliwiec ◽  
Erlan Sanchez ◽  
Charles S DeCarli ◽  
...  
Keyword(s):  
White Matter ◽  
Slow Wave ◽  
White Matter Hyperintensities ◽  
Slow Wave Sleep ◽  
Brain Volume ◽  
Brain Aging ◽  
Brain Mri ◽  
Community Based ◽  
Brain Infarcts ◽  
Absolute Duration

Objective:To test the hypothesis that reduced slow-wave sleep, or N3 sleep, which is thought to underlie the restorative functions of sleep, is associated with MRI markers of brain aging, we evaluated this relationship in the community-based Framingham Heart Study Offspring cohort using polysomnography and brain MRI.Methods:We studied 492 participants (58.8 ± 8.8 years, 49.4% male) free of neurological diseases who completed a brain MRI scan and in-home overnight polysomnography to assess slow-wave sleep (absolute duration and percentage of total sleep). Volumes of total brain, total cortical, frontal cortical, subcortical gray matter, hippocampus, and white matter hyperintensities were investigated as a percentage of intracranial volume and the presence of covert brain infarcts was evaluated. Linear and logistic regression models were adjusted for age, age squared, sex, time interval between polysomnography and MRI (3.3 ± 1.0 years), APOE4 carrier status, stroke risk factors, sleeping pill use, body mass index and depression.Results:Less slow-wave sleep was associated with lower cortical brain volume (absolute duration, β[standard error]: 0.20[0.08], p=0.015; percentage, 0.16[0.08], p=0.044), lower subcortical brain volume (percentage, 0.03[0.02], p=0.034), and higher white matter hyperintensities volume (absolute duration, -0.12[0.05], p=0.010; percentage -0.10[0.04], p=0.033). Slow-wave sleep duration was not associated with hippocampal volume or the presence of covert brain infarcts.Conclusion:Loss of slow-wave sleep might facilitate accelerated brain aging, as evidence by its association with MRI markers suggestive of brain atrophy and injury. Alternatively, subtle injuries and accelerated aging might reduce the ability of the brain to produce slow-wave sleep.

White matter hyperintensities and cognitive performance in adult patients with bipolar I, bipolar II, and major depressive disorders

2014 ◽  
Vol 29 (4) ◽  
pp. 226-232 ◽  
Author(s):  
T. Kieseppä ◽  
R. Mäntylä ◽  
A. Tuulio-Henriksson ◽  
K. Luoma ◽  
O. Mantere ◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Hyperintensities ◽  
Structural Equation Models ◽  
Brain Mri ◽  
Path Model ◽  
Control Group ◽  
Coefficient Estimate ◽  
Major Depressive ◽  
Brain White Matter ◽  
Increased Risk

AbstractPurpose:We evaluate for the first time the associations of brain white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) with neuropsychological variables among middle-aged bipolar I (BPI), II (BPII) and major depressive disorder (MDD) patients and controls using a path model.Methods:Thirteen BPI, 15 BPII, 16 MDD patients, and 21 controls underwent brain MRI and a neuropsychological examination. Two experienced neuroradiologists evaluated WMHs on the MRI scans. We constructed structural equation models to test the strength of the associations between deep WMH (DWMH) grade, neuropsychological performance and diagnostic group.Results:Belonging in the BPI group as opposed to the control group predicted higher DWMH grade (coefficient estimate 1.13, P = 0.012). The DWMH grade independently predicted worse performance on the Visual Span Forward test (coefficient estimate −0.48, P = 0.002). Group effects of BPI and MDD were significant in predicting poorer performance on the Digit Symbol test (coefficient estimate −5.57, P = 0.016 and coefficient estimate −5.66, P = 0.034, respectively).Limitations:Because of the small number of study subjects in groups, the negative results must be considered with caution.Conclusions:Only BPI patients had an increased risk for DWMHs. DWMHs were independently associated with deficits in visual attention.

scholarly journals Genome-wide scan in Hispanics highlights candidate loci for brain white matter hyperintensities

2017 ◽  
Vol 3 (5) ◽  
pp. e185 ◽  
Author(s):  
Ashley Beecham ◽  
Chuanhui Dong ◽  
Clinton B. Wright ◽  
Nicole Dueker ◽  
Adam M. Brickman ◽  
...  
Keyword(s):  
White Matter ◽  
Genetic Variants ◽  
White Matter Hyperintensities ◽  
Brain Mri ◽  
Intracranial Volume ◽  
Snp Analysis ◽  
Community Based ◽  
Brain White Matter ◽  
Genome Wide ◽  
A Genome

Objective:To investigate genetic variants influencing white matter hyperintensities (WMHs) in the understudied Hispanic population.Methods:Using 6.8 million single nucleotide polymorphisms (SNPs), we conducted a genome-wide association study (GWAS) to identify SNPs associated with WMH volume (WMHV) in 922 Hispanics who underwent brain MRI as a cross-section of 2 community-based cohorts in the Northern Manhattan Study and the Washington Heights–Inwood Columbia Aging Project. Multiple linear modeling with PLINK was performed to examine the additive genetic effects on ln(WMHV) after controlling for age, sex, total intracranial volume, and principal components of ancestry. Gene-based tests of association were performed using VEGAS. Replication was performed in independent samples of Europeans, African Americans, and Asians.Results:From the SNP analysis, a total of 17 independent SNPs in 7 genes had suggestive evidence of association with WMHV in Hispanics (p < 1 × 10−5) and 5 genes from the gene-based analysis with p < 1 × 10−3. One SNP (rs9957475 in GATA6) and 1 gene (UBE2C) demonstrated evidence of association (p < 0.05) in the African American sample. Four SNPs with p < 1 × 10−5 were shown to affect binding of SPI1 using RegulomeDB.Conclusions:This GWAS of 2 community-based Hispanic cohorts revealed several novel WMH-associated genetic variants. Further replication is needed in independent Hispanic samples to validate these suggestive associations, and fine mapping is needed to pinpoint causal variants.

Prospective associations between white matter hyperintensities and lower extremity function

Neurology ◽  
2018 ◽  
Vol 90 (15) ◽  
pp. e1291-e1297 ◽  
Author(s):  
So Young Moon ◽  
Philipe de Souto Barreto ◽  
Yves Rolland ◽  
Marie Chupin ◽  
Ali Bouyahia ◽  
...  
Keyword(s):  
Older Adults ◽  
White Matter ◽  
Lower Extremity ◽  
White Matter Hyperintensities ◽  
Brain Mri ◽  
Community Dwelling ◽  
Lower Extremity Function ◽  
Mixed Effect ◽  
Extremity Function ◽  
Slow Progression

ObjectiveTo evaluate the relationship of white matter hyperintensities (WMH) with decline in lower extremity function (LEF) over approximately 3 years in dementia-free older adults with memory complaints.MethodsWe obtained brain MRI data from 458 community-dwelling adults, aged 70 years or over, at baseline, and from 358 adults over an average follow-up of 963 days. We evaluated LEF using the Short Physical Performance Battery (SPPB). We related baseline WMH volumes and progression to SPPB scores over time, using mixed-effect linear regressions. For the secondary analyses, we categorized baseline WMH volume into quartiles, and dichotomized the WMH progression to compare fast and slow progression.ResultsBaseline WMH volume (β = −0.017, 95% confidence interval [CI] −0.025 to −0.009), as well as WMH progression (β = −0.002, 95% CI −0.003 to −0.001), significantly associated with a decline in SPPB performance in adjusted analyses. Compared with the lowest quartile of baseline WMH volume, the highest quartile associated with a decline in SPPB performance (β = −0.301, 95% CI −0.558 to −0.044). Fast progression also associated with a decline in SPPB performance. We found clinically meaningful differences in the SPPB, with higher scores in participants with slow progression of WMH, at both 24 and 36 months.ConclusionsBaseline level and WMH progression associated with longitudinal decline in SPPB performance among older adults. We detected clinically meaningful differences in SPPB performance on comparing fast with slow progression of WMH, suggesting that speed of WMH progression is an important determinant of LEF during aging.

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

2019 ◽  
Author(s):  
Muhammad Febrian Rachmadi ◽  
Maria del C. Valdés-Hernández ◽  
Stephen Makin ◽  
Joanna Wardlaw ◽  
Taku Komura
Keyword(s):  
Deep Learning ◽  
White Matter ◽  
Gaussian Noise ◽  
White Matter Hyperintensities ◽  
Learning Algorithms ◽  
Brain Mri ◽  
Supervised Machine Learning ◽  
Disease Evolution ◽  
Auxiliary Input

AbstractPrevious 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 model’s 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.

Abstract MP46: Growth Factors Are Associated With Imaging Markers of Brain Aging in Young Adults

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Claudia L Satizabal ◽  
Alexa Beiser ◽  
Jayandra J Himali ◽  
Rhoda Au ◽  
Philip A Wolf ◽  
...  
Keyword(s):  
Young Adults ◽  
Growth Factors ◽  
White Matter ◽  
Growth Factor ◽  
White Matter Hyperintensities ◽  
Brain Aging ◽  
Brain Mri ◽  
Blood Draw ◽  
Brain Volumes ◽  
Angiopoietin 2

Metabolic and vascular dysregulation are related to stroke, cognitive decline and dementia. Growth factor biomarkers of these processes, such as Insulin-like Growth Factor 1 (IGF1) and Vascular Endothelial Growth Factor (VEGF) have been associated with risk of neurodegeneration and stroke in middle-aged and older Framingham participants. Additionally, hepatocyte growth factor (HGF) and angiopoietin 2 are novel biomarkers of interest as they have been related to cardiovascular events. As abnormal brain changes probably start years before clinical symptoms, we hypothesize that circulating growth factors are related to MRI endophenotypes of brain aging. We included 1,877 individuals aged 46±8 years from the Framingham Study. Blood samples were collected during 2008-2011, and used to measure IGF1, VEGF, HGF, angiopoietin 2 and its receptor tyrosine kinase (TIE2). Participants underwent brain MRI examination (2009-2013) from which brain volumes and white matter hyperintensities were estimated. We related growth factor levels to brain MRI markers adjusting for age, sex, time between blood draw and MRI, and cardiovascular risk factors. Lower IGF1, as well as higher HGF and angiopoietin 2 levels were associated with higher ventricular volumes indicative of brain shrinkage. Higher TIE2 levels were associated with lower total brain and gray matter volumes, while higher angiopoietin 2 levels were associated with lower white matter volumes. Lower IGF1 levels were also associated with reduced hippocampal volumes. Finally, higher TIE2 levels were associated with larger white matter hyperintensities. Our results suggest that growth factors are associated with neurodegenerative and cerebrovascular markers of brain aging in healthy young adults. Whereas IGF1 seems protective, higher levels of HGF, angiopoietin 2 and TIE2 were associated with greater subclinical brain injury. These associations expand our understanding of the earliest stages of brain aging. We will extend our findings by analyzing cognitive outcomes.

scholarly journals IC-P-081: Quantification of White Matter Hyperintensities Using A Multi-Modality Automated Segmentation Method

2016 ◽  
Vol 12 ◽  
pp. P63-P63
Author(s):  
Richard Joules ◽  
Katherine R. Gray ◽  
Natalie Royle ◽  
Robin Wolz ◽  
Derek L. Hill
Keyword(s):  
White Matter ◽  
White Matter Hyperintensities ◽  
Automated Segmentation ◽  
Segmentation Method
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