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  • Ovid Technologies (Wolters Kluwer Health)  (14)
  • Sharma, Pankaj  (14)
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  • Ovid Technologies (Wolters Kluwer Health)  (14)
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  • 1
    Online Resource
    Online Resource
    Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting
    Ovid Technologies (Wolters Kluwer Health) ; 2019
    In:  Neurology Vol. 92, No. 5 ( 2019-01-29)
    Details
    In: Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 92, No. 5 ( 2019-01-29)
    Type of Medium: Online Resource
    ISSN: 0028-3878 , 1526-632X
    URL: Article
    DOI: 10.1212/WNL.0000000000006851
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2019
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  • 2
    Online Resource
    Online Resource
    Design and rationale for examining neuroimaging genetics in ischemic stroke : The MRI-GENIE study
    Ovid Technologies (Wolters Kluwer Health) ; 2017
    In:  Neurology Genetics Vol. 3, No. 5 ( 2017-10), p. e180-
    Details
    In: Neurology Genetics, Ovid Technologies (Wolters Kluwer Health), Vol. 3, No. 5 ( 2017-10), p. e180-
    Abstract: To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI–GENetics Interface Exploration (MRI-GENIE) study. Methods: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease. Conclusions: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.
    Type of Medium: Online Resource
    ISSN: 2376-7839
    URL: Article
    DOI: 10.1212/NXG.0000000000000180
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2017
    detail.hit.zdb_id: 2818607-2
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  • 3
    Online Resource
    Online Resource
    Abstract WP205: Pipeline for Automated White Matter Hyperintensity Segmentation in Patients With Acute Ischemic Stroke: The MRI-GENIE Study
    Ovid Technologies (Wolters Kluwer Health) ; 2017
    In:  Stroke Vol. 48, No. suppl_1 ( 2017-02)
    Details
    In: Stroke, Ovid Technologies (Wolters Kluwer Health), Vol. 48, No. suppl_1 ( 2017-02)
    Abstract: Introduction: White matter hyperintensity volume (WMHv) is an important and highly heritable cerebrovascular phenotype; however, manual or semi-automated approaches to clinically acquired MRI analysis hinder large-scale studies in acute ischemic stroke (AIS). In this work, we develop a high-throughput, fully automated WMHv analysis pipeline for clinical fluid-attenuated inversion recovery (FLAIR) images to facilitate rapid genetic discovery in AIS. Methods: Automated WMHv extraction from multiple subjects relies on significant pre-processing of medical scans, including co-registration of the images. To reduce the effects of anisotropic voxel sizes, each FLAIR image is upsampled using bi-cubic interpolation. Brain extraction is performed using RObust Brain EXtraction (ROBEX). Images are then registered to an in-house FLAIR template using Advanced Normalization Tools (ANTs). The spatial covariation of WMH is learned through principal component analysis (PCA) of manual outlines from 100 subjects. Areas of leukoaraiosis are identified and separated from other lesions using the PCA modes. Volumes are then computed using non-interpolated slices for each subject. Standard deviation (SD) in WMHv (9 subjects; 6 raters each) is calculated as a measure of variability. Good agreement between automated and manual outlines is assessed in 358 subjects (automated WMHv within 3SD of manual WMHv). Results: As part of the MRI - Gen etics I nterface E xploration (MRI-GENIE) study, WMHv were calculated on a set of 2703 FLAIR images of patients from 12 independent AIS cohorts (sites). Results are shown in Figure 1. Comparing manual and automated WMHv shows that 88% of the automated WMHv fall within 3 SD from the manual WMHv, suggesting good agreement. Conclusion: WMHv segmentation using a fully-automated pipeline for analysis of clinical MRIs is both feasible and accurate. Ongoing analysis of the extracted WMHv is expected to advance current knowledge of risks and outcomes in AIS.
    Type of Medium: Online Resource
    ISSN: 0039-2499 , 1524-4628
    URL: Article
    DOI: 10.1161/str.48.suppl_1.wp205
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2017
    detail.hit.zdb_id: 1467823-8
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  • 4
    Online Resource
    Online Resource
    Abstract 136: Genetics of White Matter Hyperintensity Burden in Patients With Ischemic Stroke: The MRI-GENIE Study
    Ovid Technologies (Wolters Kluwer Health) ; 2017
    In:  Stroke Vol. 48, No. suppl_1 ( 2017-02)
    Details
    In: Stroke, Ovid Technologies (Wolters Kluwer Health), Vol. 48, No. suppl_1 ( 2017-02)
    Abstract: Introduction: The MRI-Genetics Interface Exploration (MRI-GENIE) study is the first international collaboration that aims to facilitate genetic discoveries in clinical cohorts of patients with acute ischemic stroke (AIS). We have amassed the largest-to-date collection of AIS cases with brain MRI scans and genome-wide genotyping to test the role of genetic susceptibility in MRI-based cerebrovascular traits. Objective/Hypothesis: To elucidate the genetic architecture of white matter hyperintensity (WMH) burden in AIS patients. Methods: Using a novel automated algorithm, we extracted WMH volume (WMHv) from clinical MRI scans of 2704 AIS patients (age 63.1 ± 14.7 years, 60.6% male) of European ancestry. Quality control (QC) measures were undertaken per subject and per SNP, excluding subjects with non-European ancestry and poor genotyping, as well as SNPs deviating from Hardy-Weinberg equilibrium and high levels of missingness. Imputation to the Haplotype Reference Consortium (HRC version r1.1) was conducted for 1712 remaining subjects with 2.8 million SNPs on the Michigan Imputation Server. After exclusion of poorly imputed SNPs (R 2 〈 0.5) and SNPs with minor allele frequency 〈 1%, 7.7 million SNPs remained for further analysis. Genome-wide association testing of natural log-transformed WMHv on the allelic dosage per SNP was adjusted for age, sex and principal components 1-10. Results: Genome-wide association testing has identified a novel locus on chromosome 2 (T allele at rs72856504) near the LDL Receptor related Protein 1B gene (LRP1B) that was significantly associated with WMHv burden in AIS (β=0.54, SE=0.098, p=3.65*10 -8 ). Conclusion: We have identified a novel locus (T allele rs72856504) on chromosome 2 near the LRP1B gene, which is specific for WMH in AIS and has not been previosuly described in stroke-free WMH cohorts. A replication effort involving additional independent cohorts of AIS patients with brain MRI and genome-wide genotyping is ongoing.
    Type of Medium: Online Resource
    ISSN: 0039-2499 , 1524-4628
    URL: Article
    DOI: 10.1161/str.48.suppl_1.136
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2017
    detail.hit.zdb_id: 1467823-8
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  • 5
    Online Resource
    Online Resource
    Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome
    Ovid Technologies (Wolters Kluwer Health) ; 2022
    In:  Neurology Vol. 99, No. 13 ( 2022-09-27), p. e1364-e1379
    Details
    In: Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 99, No. 13 ( 2022-09-27), p. e1364-e1379
    Abstract: To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern–specific ways. Methods MR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3–6 months after stroke were obtained from the MRI–Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning–based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score 〉 2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age 2 , sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts. Results A total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location–specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location. Discussion Higher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.
    Type of Medium: Online Resource
    ISSN: 0028-3878 , 1526-632X
    URL: Article
    DOI: 10.1212/WNL.0000000000200926
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype
    Ovid Technologies (Wolters Kluwer Health) ; 2020
    In:  Neurology Vol. 95, No. 1 ( 2020-07-07), p. e79-e88
    Details
    In: Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 95, No. 1 ( 2020-07-07), p. e79-e88
    Abstract: To examine etiologic stroke subtypes and vascular risk factor profiles and their association with white matter hyperintensity (WMH) burden in patients hospitalized for acute ischemic stroke (AIS). Methods For the MRI Genetics Interface Exploration (MRI-GENIE) study, we systematically assembled brain imaging and phenotypic data for 3,301 patients with AIS. All cases underwent standardized web tool–based stroke subtyping with the Causative Classification of Ischemic Stroke (CCS). WMH volume (WMHv) was measured on T2 brain MRI scans of 2,529 patients with a fully automated deep-learning trained algorithm. Univariable and multivariable linear mixed-effects modeling was carried out to investigate the relationship of vascular risk factors with WMHv and CCS subtypes. Results Patients with AIS with large artery atherosclerosis, major cardioembolic stroke, small artery occlusion (SAO), other, and undetermined causes of AIS differed significantly in their vascular risk factor profile (all p 〈 0.001). Median WMHv in all patients with AIS was 5.86 cm 3 (interquartile range 2.18–14.61 cm 3 ) and differed significantly across CCS subtypes ( p 〈 0.0001). In multivariable analysis, age, hypertension, prior stroke, smoking (all p 〈 0.001), and diabetes mellitus ( p = 0.041) were independent predictors of WMHv. When adjusted for confounders, patients with SAO had significantly higher WMHv compared to those with all other stroke subtypes ( p 〈 0.001). Conclusion In this international multicenter, hospital-based cohort of patients with AIS, we demonstrate that vascular risk factor profiles and extent of WMH burden differ by CCS subtype, with the highest lesion burden detected in patients with SAO. These findings further support the small vessel hypothesis of WMH lesions detected on brain MRI of patients with ischemic stroke.
    Type of Medium: Online Resource
    ISSN: 0028-3878 , 1526-632X
    URL: Article
    DOI: 10.1212/WNL.0000000000009728
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2020
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  • 7
    Online Resource
    Online Resource
    The Association of SUR1 Polymorphisms with Acute Infarct Size: The MRI-GENIE Study (1348)
    Ovid Technologies (Wolters Kluwer Health) ; 2020
    In:  Neurology Vol. 94, No. 15_supplement ( 2020-04-14)
    Details
    In: Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 94, No. 15_supplement ( 2020-04-14)
    Type of Medium: Online Resource
    ISSN: 0028-3878 , 1526-632X
    URL: Article
    DOI: 10.1212/WNL.94.15_supplement.1348
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2020
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  • 8
    Online Resource
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    Abstract WMP56: Genetics of Acute Ischemic Lesion Volume: the MRI-Genetics Interface Exploration (MRI-GENIE) Study
    Ovid Technologies (Wolters Kluwer Health) ; 2018
    In:  Stroke Vol. 49, No. Suppl_1 ( 2018-01-22)
    Details
    In: Stroke, Ovid Technologies (Wolters Kluwer Health), Vol. 49, No. Suppl_1 ( 2018-01-22)
    Abstract: Introduction: Genome-wide association (GWA) studies have contributed substantially to the understanding of complex vascular traits including ischemic stroke; however, genetic determinants of acute cerebral ischemia remain to be elucidated. Objective: To investigate the genetic architecture of cerebral infarct lesion burden in patients with acute ischemic stroke (AIS). Methods: Twelve international sites contributed 3,301 AIS subjects with acute MRI and genome-wide genotyping to the MRI-GENIE study. In a preliminary analysis of 657 subjects of European ancestry, acute cerebral infarct lesions were outlined on diffusion-weighted images (DWI) using a semi-automated volumetric method. Standard quality control measures were performed per single nucleotide polymorphism (SNP) and per subject. SNPs were imputed to the Haplotype Reference Consortium v1.1 panel. Natural log-transformed DWI volume was used for GWA analysis with allelic dosage per SNP, age, sex and principal components 1-5 of genetic ancestry as covariates. Results: GWA testing for DWI volume in 7.7 million SNPs yielded no signal crossing the Bonferroni-corrected genome-wide significance threshold of p 〈 5*10 -8 . However, several loci passed the nominal significance threshold of p 〈 1*10 -6 (Figure 1), including a locus on chromosome 2 in the SPATA3-AS1 (spermatogenesis associated 3 antisense RNA 1) gene (lead SNP: rs2368999, MAF=36%, p =8.4*10 -7 ) and an intronic locus on chromosome 10 in the ATRNL1 (Attractin like 1) gene (lead SNP: rs592284, MAF=10%, p =9.1*10 -7 ). Conclusion: In this first-to-date, preliminary GWAS of acute cerebral infarct volume in AIS patients, we identified several new, nominally significant loci including a locus in ATRNL1, a gene previously linked to carotid plaque burden. Further analyses of the MRI-GENIE cohort to replicate these findings, to include stroke subtype specific analyses, and to increase the overall statistical power of the study are ongoing.
    Type of Medium: Online Resource
    ISSN: 0039-2499 , 1524-4628
    URL: Article
    DOI: 10.1161/str.49.suppl_1.WMP56
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2018
    detail.hit.zdb_id: 1467823-8
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  • 9
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    Online Resource
    Abstract WP204: Genetic Variant in VCAM1 Mediates Acute Infarct Size in Ischemic Stroke Patients
    Ovid Technologies (Wolters Kluwer Health) ; 2017
    In:  Stroke Vol. 48, No. suppl_1 ( 2017-02)
    Details
    In: Stroke, Ovid Technologies (Wolters Kluwer Health), Vol. 48, No. suppl_1 ( 2017-02)
    Abstract: Introduction: Even though neuroinflammation is increasingly recognized as an essential contributor to ischemic brain injury, the exact underlying mechanisms remain unclear. Higher expression of the vascular cell adhesion molecule 1 (VCAM-1) increases leukocyte-brain endothelium interaction and has been associated with larger infarct size following acute ischemic stroke (AIS). The activation of the TGF-β signaling pathways can down-regulate VCAM-1 expression and ameliorate deleterious tissue outcome during neuroinflammation. Hypothesis: We sought to investigate whether genetic variation in the TGF-beta pathway and adhesion molecule genes is associated with acute stroke lesion size. Methods: We completed genome-wide association (GWA) testing and diffusion-weighted imaging lesion volume (DWIv) analysis in a discovery cohort of 532 AIS patients of European ancestry enrolled within 48 hours of symptom onset. An independent European ancestry cohort of 724 AIS patients with GWA data and automated DWIv served as replication cohort. GWA testing per SNP was performed using linear regression modeling of natural log-transformed DWIv adjusted for age, sex and relevant principal components. We selected 42 inflammatory genes in the TGF-beta pathway for a gene-based analysis using VEGAS (Versatile Gene-based -Association Study) software. A pre-specified discovery phase Bonferroni-corrected threshold was set at p 〈 0.001. In the replication phase, 14 SNPs overlapping were tested at the Bonferroni-corrected p-value threshold of p 〈 0.004. Results: Of all genes in the TGF-beta pathway, VCAM1 (p=0.0006) was significantly associated with DWIv in the discovery AIS cohort (age: 66 ± 14.9 years, sex: 63.4% male, DWIv: 2.2cm 3 (IQR: 0.6-11.7cm 3 )). A single SNP within the VCAM1 gene boundaries (rs3176876 (BETA=0.2341, p=0.003)) was significantly associated with DWIv in the replication AIS cohort (age: 65 ± 14.1 years, sex: 68.6% male, DWIv: 4cm 3 (IQR: 1.3-17.2cm 3 )). Conclusion: The genetic variant rs3176876 in the VCAM1 gene is associated with larger infarct lesion size measured on brain MRI of AIS patients. These findings suggest genetic contribution to the pro-inflammatory mechanisms in acute cerebral ischemia and warrant further investigation.
    Type of Medium: Online Resource
    ISSN: 0039-2499 , 1524-4628
    URL: Article
    DOI: 10.1161/str.48.suppl_1.wp204
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2017
    detail.hit.zdb_id: 1467823-8
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  • 10
    Online Resource
    Online Resource
    Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
    Ovid Technologies (Wolters Kluwer Health) ; 2019
    In:  Stroke Vol. 50, No. 7 ( 2019-07), p. 1734-1741
    Details
    In: Stroke, Ovid Technologies (Wolters Kluwer Health), Vol. 50, No. 7 ( 2019-07), p. 1734-1741
    Abstract: We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods— Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms’ performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results— The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P 〈 0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm 3 (0.9–16.6 cm 3 ). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P 〈 0.0001) and different topography compared with other stroke subtypes. Conclusions— Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
    Type of Medium: Online Resource
    ISSN: 0039-2499 , 1524-4628
    URL: Article
    DOI: 10.1161/STROKEAHA.119.025373
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2019
    detail.hit.zdb_id: 1467823-8
    Location Call Number Limitation Availability
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