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ShareScore release 0.9.0
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25 results for “Fat Fraction”
Longitudinal evaluation of a cohort of fascioscapulohumeral disease subjects and evaluation of predictive power of fat fraction and water T2 on fat replacement over time
<p>Introduction</p> <p>The purpose of our work was to longitudinally evaluate disease involvement over time of a cohort of patients with facioscapulohumeral disease (FSHD) using a quantitative lower limb muscle MRI protocol and to explore whether fat fraction (FF) and water T2 (wT2) at baseline were predictive of long-term fat replacement.</p> <p>Methods</p> <p>Thirty FSHD subjects with at least an edematous muscle at MRI were enrolled and scanned at baseline and at<span> </span>6, 12 and 24 months of follow-up. FF and wT2 were calculated in twelve muscles of the thigh and six muscles of the leg. FF and wT2 were analyzed longitudinally and correlated with standard clinical scores. FF and wT2 at baseline were also evaluated as predictors over fat replacement over the same time period. Analyses were run at whole-thigh and leg level compartment and single-muscle level.</p> <p>Results</p> <p>Yearly, FF showed a mean increase of 2% whereas wT2 decreased on average by 1-2 ms. The compartments with intermediate and higher baseline FF showed the greater increase of FF at 24 months; those with higher baseline wT2 showed the greater FF increase at follow-up. FF and wT2 anticorrelated with 6-minute-walking-test and dynamometry and positively correlated with clinical severity score.</p> <p>Conclusions</p> <p>Our results suggest that intermediate and higher FF and higher wT2 at baseline are the strongest predictors of higher fat replacement over time.</p>
UDFF Performance Evaluation ((Ultrason Derived Fat Fraction)
ClinicalTrials.gov study NCT06080386. IPD Sharing: NO. Countries: 1. Publications: 0.
Synovial Tissue and Fat Pad Stromal Vascular Fraction Bioengineering in Patients With Knee Articular Cartilage Injury
ClinicalTrials.gov study NCT05120700. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Single-cell RNA-seq of eWAT stromal vascular fraction cells from WT and Camk2d myeloid conditional knockout mice after high-fat diet
GEO Series GSE316934. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy
<p><strong>Introduction</strong>. This database includes the radiomic features used as covariates to train machine learning algorithms in the paper “ Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy”.</p> <p><strong>Purpose</strong>. Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning.</p> <p><strong>Method</strong>. Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset.</p> <p><strong>Results</strong>. Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2.</p> <p><strong>Conclusion</strong>. This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.