| 1. |
- Bonkhoff, Anna K, et al.
(författare)
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The relevance of rich club regions for functional outcome post-stroke is enhanced in women.
- 2023
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Ingår i: Human brain mapping. - : Wiley. - 1097-0193 .- 1065-9471. ; 44:4, s. 1579-1592
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.
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| 2. |
- Bourached, Anthony, et al.
(författare)
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Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity
- 2023
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Ingår i: BRAIN COMMUNICATIONS. - 2632-1297. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by similar to 20% when increasing the sample size 9x [maximum for 100 patients: 0.279 +/- 0.005 (R2, 95% confidence interval), 900 patients: 0.337 +/- 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes. Bourached et al. contrast linear and deep learning-based algorithms in their prediction performances of stroke severity depending on the training set sample sizes. They find that linear regression outperforms deep learning-based algorithms for smaller training samples comprising lesion location information of 100 patients, while deep learning excels in the case of larger samples (N = 900).
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| 3. |
- Bretzner, Martin, et al.
(författare)
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Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.
- 2023
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Ingår i: Neurology. - 1526-632X .- 0028-3878. ; 100:8, s. e822-e833
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Tidskriftsartikel (refereegranskat)abstract
- While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input.We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.
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| 4. |
- Johansson, Malin, et al.
(författare)
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Genetic Predisposition to Mosaic Chromosomal Loss Is Associated with Functional Outcome after Ischemic Stroke
- 2021
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Ingår i: Neurology: Genetics. - 2376-7839. ; 7:6
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Tidskriftsartikel (refereegranskat)abstract
- Background and ObjectivesTo test the hypothesis that a predisposition to acquired genetic alterations is associated with ischemic stroke outcome by investigating the association between a polygenic risk score (PRS) for mosaic loss of chromosome Y (mLOY) and outcome in a large international data set.MethodsWe used data from the genome-wide association study performed within the Genetics of Ischemic Stroke Functional Outcome network, which included 6,165 patients (3,497 men and 2,668 women) with acute ischemic stroke of mainly European ancestry. We assessed a weighted PRS for mLOY and examined possible associations with the modified Rankin Scale (mRS) score 3 months poststroke in logistic regression models. We investigated the whole study sample as well as men and women separately.ResultsIncreasing PRS for mLOY was associated with poor functional outcome (mRS score >2) with an odds ratio (OR) of 1.11 (95% confidence interval [CI] 1.03-1.19) per 1 SD increase in the PRS after adjustment for age, sex, ancestry, stroke severity (NIH Stroke Scale), smoking, and diabetes mellitus. In sex-stratified analyses, we found a statistically significant association in women (adjusted OR 1.20, 95% CI 1.08-1.33). In men, the association was in the same direction (adjusted OR 1.04, 95% CI 0.95-1.14), and we observed no significant genotype-sex interaction.DiscussionIn this exploratory study, we found associations between genetic variants predisposing to mLOY and stroke outcome. The significant association in women suggests underlying mechanisms related to genomic instability that operate in both sexes. These findings need replication and mechanistic exploration.
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| 5. |
- Lagging, Cecilia, et al.
(författare)
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APOE ε4 is associated with younger age at ischemic stroke onset but not with stroke outcome
- 2019
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Ingår i: Neurology. - 1526-632X. ; 93:19, s. 849-853
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Tidskriftsartikel (refereegranskat)abstract
- Stroke outcome is determined by a complex interplay, where age and stroke severity are predominant predictors. Studies on hemorrhagic stroke indicate that APOE genotype is a predictor of poststroke outcomes,1,2 but results from studies on ischemic stroke are more conflicting.1,3 There is 1 study suggesting an influence of APOE genotype on age at ischemic stroke onset,4 and sex-specific effects on outcome have been reported.5 Taken together, there is a need for larger studies on APOE and ischemic stroke outcomes with integrated information on age, severity, and sex.The 3 common APOE alleles ε2, ε3, and ε4 can be separated by a combination of 2 single nucleotide polymorphisms (SNPs), rs429358 and rs7412. Thus, associations with APOE alleles are not directly captured in a regular genome-wide association study (GWAS), where each SNP is investigated separately. We derived the 3 common APOE alleles and investigated the interplay between APOE, age at ischemic stroke onset, severity, sex, and outcome within a large international collaboration, the Genetics of Ischaemic Stroke Functional Outcome (GISCOME) network.
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| 6. |
- Traylor, Matthew, et al.
(författare)
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Genetic Variation at 16q24.2 is associated with small vessel stroke.
- 2017
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Ingår i: Annals of neurology. - : Wiley. - 1531-8249 .- 0364-5134. ; 81:3
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises a quarter of all ischaemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown younger onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age-at-onset informed GWAS meta-analysis, including a large younger onset SVS population, to identify novel associations with stroke.We used a three-stage age-at-onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on mRNA expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain.We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (OR(95% CI)=1.16(1.10-1.22); p=3.2x10(-9) ). The lead SNP (rs12445022) was also associated with cerebral white matter hyperintensities (OR(95% CI)=1.10(1.05-1.16); p=5.3x10(-5) ; N=3,670), but not intracerebral haemorrhage (OR(95% CI)=0.97(0.84-1.12); p=0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues (p=9.4x10(-7) ), and DNA methylation at probe cg16596957 in whole blood (p=5.3x10(-6) ).16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. This article is protected by copyright. All rights reserved.
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