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Dataset results
51 results for “Facioscapulohumeral Muscular Dystrophy”
New Clinical Outcome Measures to Remotely Evaluate Patients With FacioScapuloHumeral Muscular Dystrophy
ClinicalTrials.gov study NCT05812144. IPD Sharing: NO. Countries: 1. Publications: 0.
Transcriptional profiling in facioscapulohumeral muscular dystrophy to identify candidate biomarkers
GEO Series GSE36398. Homo sapiens. 50 samples. Type: Expression profiling by array.
Rbfox1 downregulation and altered Calpain 3 splicing by FRG1 in a mouse model of facioscapulohumeral muscular dystrophy (FSHD).
GEO Series GSE32073. Mus musculus. 24 samples. Type: Expression profiling by array.
Randomized Study of Albuterol in Patients With Facioscapulohumeral Muscular Dystrophy
ClinicalTrials.gov study NCT00004685. IPD Sharing: Not stated. Countries: 0. Publications: 0.
P38α Regulates Expression of DUX4 in Facioscapulohumeral Muscular Dystrophy
GEO Series GSE153301. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Hit-and-run silencing of endogenous DUX4 by targeting DNA hypomethylation on D4Z4 repeats in facioscapulohumeral muscular dystrophy [RNA-seq]
GEO Series GSE201178. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Hit-and-run silencing of endogenous DUX4 by targeting DNA hypomethylation on D4Z4 repeats in facioscapulohumeral muscular dystrophy
GEO Series GSE201185. Homo sapiens. 11 samples. Type: Methylation profiling by genome tiling array; 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>
Dynamic magnetic resonance imaging of muscle contraction in facioscapulohumeral muscular dystrophy
<p>This database includes the raw data linked with paper “ Dynamic magnetic resonance imaging of muscle contraction in facioscapulohumeral muscular dystrophy”.<br> Data are related to FSHD patients, who had a confirmed molecular diagnosis. All subjects were scanned on a 3T MAGNETOM Skyra [Siemens Healthineers]. Dynamic scans were performed for both thighs separately in addition to quantitative sequence T2-mapping and Fat Fraction mapping.<br> Quantitative muscle MRI (water-T2 and fat mapping) is being increasingly used to assess disease involvement in muscle disorders, while imaging techniques for assessment of the dynamic and elastic muscle properties have not yet been translated into clinics. In this exploratory study, we quantitatively characterized muscle deformation (strain) in patients affected by facioscapulohumeral muscular dystrophy (FSHD), a prevalent muscular dystrophy, by applying dynamic MRI synchronized with neuromuscular electrical stimulation (NMES). We evaluated the quadriceps muscles in 34 ambulatory patients and 13 healthy controls, at 6-to 12-month time intervals. While a subgroup of patients behaved similarly to controls, for another subgroup the median strain decreased over time (approximately 57% over 1.5 years). Dynamic MRI parameters did not correlate with quantitative MRI. Our results suggest that the evaluation of muscle contraction by NMES-MRI is feasible and could potentially be used to explore the elastic properties and monitor muscle involvement in FSHD and other neuromuscular disorders.</p>
Muscle Diffusion Tensor Imaginig in Facioscapulohumeral muscular dystrophy
<p>Introduction/Aims</p> <p>Muscle diffusion tensor imaging (mDTI) has not yet been explored in facioscapulohumeral muscular dystrophy (FSHD). We aimed to assess diffusivity parameters in FSHD subjects compared to healthy controls (HCs), with regard to their ability to precede any fat replacement process in the muscle.</p> <p>Methods</p> <p>Fat Fraction (FF), water T2 (wT2), Mean, Radial and Axial Diffusivity (MD, RD, AD) and Fractional Anisotropy (FA) of thigh muscles were calculated in a cohort of ten FSHD subjects and fifteen age-matched HCs. All parameters were compared between FSHD subjects and controls, exploring also their values along the main axis of the muscle. Diffusivity parameters were tested as predictors of disease involvement in muscle compartments with no significant fat substitution and edema. Whole-thigh mDTI values were correlated to clinical severity scores.</p> <p> </p> <p>Results</p> <p>FF and wT2 were significantly higher in FSHD than controls whereas MD, RD and AD were significantly lower than controls (p < .05). No difference with controls was shown for FA. FF positively correlated with FA and negatively with MD, RD and AD. FF and FA showed significantly higher values distally than proximally. whereas wT2, MD, RD, AD showed lower values distally than proximally (p<.05). Muscles with no significant fat replacement or edema showed a significantly lower AD and FA than controls. FA was the only parameter to positively correlate with the 6-Minute Walking Test.</p> <p> </p> <p>Discussion</p> <p>Our results suggest that mDTI parameters appear to predominantly reflect fat replacement in FSHD and might be able to show disease involvement in muscles even before significant fat replacement.</p>
Hit-and-run silencing of endogenous DUX4 by targeting DNA hypomethylation on D4Z4 repeats in facioscapulohumeral muscular dystrophy [EPIC Methylation]
GEO Series GSE199690. Homo sapiens. 7 samples. Type: Methylation profiling by genome tiling array.
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DANDI Archive for NWB datasets
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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.