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- Thompson, Paul M., et al.
(författare)
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The ENIGMA Consortium : large-scale collaborative analyses of neuroimaging and genetic data
- 2014
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Ingår i: BRAIN IMAGING BEHAV. - : Springer Science and Business Media LLC. - 1931-7557 .- 1931-7565. ; 8:2, s. 153-182
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Tidskriftsartikel (refereegranskat)abstract
- The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
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- Benton, S., et al.
(författare)
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Impact of Next-generation Sequencing on Interobserver Agreement and Diagnosis of Spitzoid Neoplasms
- 2021
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Ingår i: American Journal of Surgical Pathology. - : Ovid Technologies (Wolters Kluwer Health). - 0147-5185 .- 1532-0979. ; 45:12, s. 1597-1605
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Tidskriftsartikel (refereegranskat)abstract
- Atypical Spitzoid melanocytic tumors are diagnostically challenging. Many studies have suggested various genomic markers to improve classification and prognostication. We aimed to assess whether next-generation sequencing studies using the Tempus xO assay assessing mutations in 1711 cancer-related genes and performing whole transcriptome mRNA sequencing for structural alterations could improve diagnostic agreement and accuracy in assessing neoplasms with Spitzoid histologic features. Twenty expert pathologists were asked to review 70 consultation level cases with Spitzoid features, once with limited clinical information and again with additional genomic information. There was an improvement in overall agreement with additional genomic information. Most significantly, there was increase in agreement of the diagnosis of conventional melanoma from moderate (kappa=0.470, SE=0.0105) to substantial (kappa=0.645, SE=0.0143) as measured by an average Cohen kappa. Clinical follow-up was available in all 70 cases which substantiated that the improved agreement was clinically significant. Among 3 patients with distant metastatic disease, there was a highly significant increase in diagnostic recognition of the cases as conventional melanoma with genomics (P<0.005). In one case, none of 20 pathologists recognized a tumor with BRAF and TERT promoter mutations associated with fatal outcome as a conventional melanoma when only limited clinical information was provided, whereas 60% of pathologists correctly diagnosed this case when genomic information was also available. There was also a significant improvement in agreement of which lesions should be classified in the Spitz category/WHO Pathway from an average Cohen kappa of 0.360 (SE=0.00921) to 0.607 (SE=0.0232) with genomics.
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- De Luca, Alberto, et al.
(författare)
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
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Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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- Matsushita, Kunihiro, et al.
(författare)
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Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes : a collaborative meta-analysis of individual participant data
- 2015
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Ingår i: LANCET DIABETES & ENDOCRINOLOGY. - 2213-8587. ; 3:7, s. 514-525
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Tidskriftsartikel (refereegranskat)abstract
- Background The usefulness of estimated glomerular filtration rate (eGFR) and albuminuria for prediction of cardiovascular outcomes is controversial. We aimed to assess the addition of creatinine-based eGFR and albuminuria to traditional risk factors for prediction of cardiovascular risk with a meta-analytic approach. Methods We meta-analysed individual-level data for 637 315 individuals without a history of cardiovascular disease from 24 cohorts (median follow-up 4.2-19.0 years) included in the Chronic Kidney Disease Prognosis Consortium. We assessed C statistic difference and reclassification improvement for cardiovascular mortality and fatal and non-fatal cases of coronary heart disease, stroke, and heart failure in a 5 year timeframe, contrasting prediction models for traditional risk factors with and without creatinine-based eGFR, albuminuria (either albumin-to-creatinine ratio [ACR] or semi-quantitative dipstick proteinuria), or both. Findings The addition of eGFR and ACR significantly improved the discrimination of cardiovascular outcomes beyond traditional risk factors in general populations, but the improvement was greater with ACR than with eGFR, and more evident for cardiovascular mortality (C statistic difference 0.0139 [95% CI 0.0105- 0.0174] for ACR and 0.0065 [0.0042-0.0088] for eGFR) and heart failure (0.0196 [0.0108-0.0284] and 0.0109 [0.0059-0.0159]) than for coronary disease (0.0048 [0.0029-0.0067] and 0.0036 [0.0019-0.0054]) and stroke (0.0105 [0.0058-0.0151]and 0.0036 [0.0004-0.0069]). Dipstick proteinuria showed smaller improvement than ACR. The discrimination improvement with eGFR or ACR was especially evident in individuals with diabetes or hypertension, but remained significant with ACR for cardiovascular mortality and heart failure in those without either of these disorders. In individuals with chronic kidney disease, the combination of eGFR and ACR for risk discrimination outperformed most single traditional predictors; the C statistic for cardiovascular mortality fell by 0.0227 (0.0158-0.0296) after omission of eGFR and ACR compared with less than 0.007 for any single modifiable traditional predictor. Interpretation Creatinine-based eGFR and albuminuria should be taken into account for cardiovascular prediction, especially when these measures are already assessed for clinical purpose or if cardiovascular mortality and heart failure are outcomes of interest. ACR could have particularly broad implications for cardiovascular prediction. In populations with chronic kidney disease, the simultaneous assessment of eGFR and ACR could facilitate improved classification of cardiovascular risk, supporting current guidelines for chronic kidney disease. Our results lend some support to also incorporating eGFR and ACR into assessments of cardiovascular risk in the general population.
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| 12. |
- Moore, Elizabeth E, et al.
(författare)
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Axonal Injury Partially Mediates Associations Between Increased Left Ventricular Mass Index and White Matter Damage.
- 2021
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Ingår i: Stroke. - 1524-4628. ; 53:3, s. 808-816
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Tidskriftsartikel (refereegranskat)abstract
- Left ventricular (LV) mass index is a marker of subclinical LV remodeling that relates to white matter damage in aging, but molecular pathways underlying this association are unknown. This study assessed if LV mass index related to cerebrospinal fluid (CSF) biomarkers of microglial activation (sTREM2 [soluble triggering receptor expressed on myeloid cells 2]), axonal injury (NFL [neurofilament light]), neurodegeneration (total-tau), and amyloid-β, and whether these biomarkers partially accounted for associations between increased LV mass index and white matter damage. We hypothesized higher LV mass index would relate to greater CSF biomarker levels, and these pathologies would partially mediate associations with cerebral white matter microstructure.Vanderbilt Memory and Aging Project participants who underwent cardiac magnetic resonance, lumbar puncture, and diffusion tensor imaging (n=142, 72±6 years, 37% mild cognitive impairment [MCI], 32% APOE-ε4 positive, LV mass index 51.4±8.1 g/m2, NFL 1070±588 pg/mL) were included. Linear regressions and voxel-wise analyses related LV mass index to each biomarker and diffusion tensor imaging metrics, respectively. Follow-up models assessed interactions with MCI and APOE-ε4. In models where LV mass index significantly related to a biomarker and white matter microstructure, we assessed if the biomarker mediated white matter associations.Among all participants, LV mass index was unrelated to CSF biomarkers (P>0.33). LV mass index interacted with MCI (P=0.01), such that higher LV mass index related to increased NFL among MCI participants. Associations were also present among APOE-ε4 carriers (P=0.02). NFL partially mediated up to 13% of the effect of increased LV mass index on white matter damage.Subclinical cardiovascular remodeling, measured as an increase in LV mass index, is associated with neuroaxonal degeneration among individuals with MCI and APOE-ɛ4. Neuroaxonal degeneration partially reflects associations between higher LV mass index and white matter damage. Findings highlight neuroaxonal degeneration, rather than amyloidosis or microglia, may be more relevant in pathways between structural cardiovascular remodeling and white matter damage.
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