Muscle imaging in laminopathies: Synthesis study identifies meaningful muscles for follow-up

French National Protocol for diagnosis and care of facioscapulohumeral muscular dystrophy (FSHD)

Facioscapulohumeral muscular dystrophy (FSHD) is one of the most common genetically inherited myopathies in adults. It is characterized by incomplete penetrance and variable expressivity. Typically, FSHD patients display asymmetric weakness of facial, scapular, and humeral muscles that may progress to other muscle groups, particularly the abdominal and lower limb muscles. Early-onset patients display more severe muscle weakness and atrophy, resulting in a higher frequency of associated skeletal abnormalities. In these patients, multisystem involvement, including respiratory, ocular, and auditory, is more frequent and severe and may include the central nervous system. Adult-onset FSHD patients may also display some degree of multisystem involvement which mainly remains subclinical. In 95% of cases, FSHD patients carry a pathogenic contraction of the D4Z4 repeat units (RUs) in the subtelomeric region of chromosome 4 (4q35), which leads to the expression of DUX4 retrogene, toxic for muscles (FSHD1). Five percent of patients display the same clinical phenotype in association with a mutation in the SMCHD1 gene located in chromosome 18, inducing epigenetic modifications of the 4q D4Z4 repeated region and expression of DUX4 retrogene. This review highlights the complexities and challenges of diagnosing and managing FSHD, underscoring the importance of standardized approaches for optimal patient outcomes. It emphasizes the critical role of multidisciplinary care in addressing the diverse manifestations of FSHD across different age groups, from skeletal abnormalities in early-onset cases to the often-subclinical multisystem involvement in adults. With no current cure, the focus on alleviating symptoms and slowing disease progression through coordinated care is paramount.

Muscle MRI in Myotonic Dystrophy type 1 (DM1): refining muscle involvement and implications for clinical trials

BACKGROUND

Only few studies reported muscle imaging data on small cohorts of patients with Myotonic dystrophy type 1 (DM1). We aimed to investigate the muscle involvement in a large cohort of patients, to refine the pattern of muscle involvement, to better understand the pathophysiological mechanisms of muscle weakness and to identify potential imaging biomarkers for disease activity and severity.

METHODS

134 DM1 patients underwent a cross-sectional muscle MRI study. STIR and T1- sequences in lower and upper body were analysed. Fat replacement, muscle atrophy and STIR positivity were evaluated using three different scales. Correlations between MRI scores, clinical features and genetic background were investigated.

RESULTS

The most frequent pattern of muscle involvement in T1 consisted of fat replacement of the tongue, sternocleidomastoideus, paraspinalis, gluteus minimus, distal quadriceps and gastrocnemius medialis. Degree of fat replacement at MRI correlated with clinical severity and disease duration, but not with CTG expansion. Fat replacement was also detected in milder/asymptomatic patients. More than 80% of patients had STIR positive signal in muscles. Most DM1 patients also showed a variable degree of muscle atrophy regardless MRI signs of fat replacement. A subset of patients (20%) showed a "marbled" muscle appearance.

CONCLUSIONS

muscle MRI is a sensitive biomarker of disease severity also for the milder spectrum of disease. STIR hyperintensty seems to precede fat replacement in T1. Beyond fat replacement, STIR positivity, muscle atrophy and "marbled" appearance suggest further mechanisms of muscle wasting and weakness in DM1, representing additional outcome measures and therapeutical targets for forthcoming clinical trials.

Two decades of advances in muscle imaging in children: from pattern recognition of muscle diseases to quantification and machine learning approaches

Muscle imaging has progressively gained popularity in the neuromuscular field. Together with detailed clinical examination and muscle biopsy, it has become one of the main tools for deep phenotyping and orientation of etiological diagnosis. Even in the current era of powerful new generation sequencing, muscle MRI has arisen as a tool for prioritization of certain genetic entities, supporting the pathogenicity of variants of unknown significance and facilitating diagnosis in cases with an initially inconclusive genetic study. Although the utility of muscle imaging is increasingly clear, it has not reached its full potential in clinical practice. Pattern recognition is known for a number of diseases and will certainly be enhanced by the use of machine learning approaches. For instance, MRI heatmap representations might be confronted with molecular results by obtaining a probabilistic diagnosis based in each disease "MRI fingerprints". Muscle ultrasound as a screening tool and quantified techniques such as Dixon MRI seem still underdeveloped. In this paper, we aim to appraise the advances in recent years in pediatric muscle imaging and try to define areas of uncertainty and potential advances that might become standardized to be widely used in the future.

Is Gene-Size an Issue for the Diagnosis of Skeletal Muscle Disorders?

Human genes have a variable length. Those having a coding sequence of extraordinary length and a high number of exons were almost impossible to sequence using the traditional Sanger-based gene-by-gene approach. High-throughput sequencing has partly overcome the size-related technical issues, enabling a straightforward, rapid and relatively inexpensive analysis of large genes.

Several large genes (e.g. TTN, NEB, RYR1, DMD) are recognized as disease-causing in patients with skeletal muscle diseases. However, because of their sheer size, the clinical interpretation of variants in these genes is probably the most challenging aspect of the high-throughput genetic investigation in the field of skeletal muscle diseases.

The main aim of this review is to discuss the technical and interpretative issues related to the diagnostic investigation of large genes and to reflect upon the current state of the art and the future advancements in the field.

LMNA-Related Muscular Dystrophy with Clinical Intrafamilial Variability

The LMNA gene is associated to a huge broad of phenotypes, including congenital Emery-Dreifuss muscular dystrophy and late-onset LMNA-related muscular dystrophy. In these forms, muscle weakness, contractures, and cardiac impairment are common. In an autosomal dominant pedigree including 5 affected patients, NGS molecular analysis performed in 6 relatives identifies the heterozygous c.1129C>T p.Arg377Cys variant in the exon 6 of the LMNA gene in three of them. Clinical, laboratorial, imaging investigation of these affected patients showed a significant clinical variability: the father presented subclinical imaging muscular dystrophy masqueraded as radiculopathy. One of his sons presented cardiac arrhythmia, muscular weakness, elbow contractures, and intranuclear pseudoinclusions on muscle biopsy. A second son presented only decreased tendon reflexes. Two other brothers presenting myalgia and cramps were not carriers of the same mutation in the LMNA gene. Early diagnosis, considering these variable phenotype and genotype, is important for genetic counseling, as well as cardiac, and rehabilitation management.

Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury

Muscle fat infiltration (MFI) of the deep cervical spine extensors has been observed in cervical spine conditions using time-consuming and rater-dependent manual techniques. Deep learning convolutional neural network (CNN) models have demonstrated state-of-the-art performance in segmentation tasks. Here, we train and test a CNN for muscle segmentation and automatic MFI calculation using high-resolution fat-water images from 39 participants (26 female, average = 31.7 ± 9.3 years) 3 months post whiplash injury. First, we demonstrate high test reliability and accuracy of the CNN compared to manual segmentation. Then we explore the relationships between CNN muscle volume, CNN MFI, and clinical measures of pain and neck-related disability. Across all participants, we demonstrate that CNN muscle volume was negatively correlated to pain (R = -0.415, p = 0.006) and disability (R = -0.286, p = 0.045), while CNN MFI tended to be positively correlated to disability (R = 0.214, p = 0.105). Additionally, CNN MFI was higher in participants with persisting pain and disability (p = 0.049). Overall, CNN's may improve the efficiency and objectivity of muscle measures allowing for the quantitative monitoring of muscle properties in disorders of and beyond the cervical spine.

Advancements in magnetic resonance imaging-based biomarkers for muscular dystrophy

Recent years have seen steady progress in the identification of genetic muscle diseases as well as efforts to develop treatment for these diseases. Consequently, sensitive and objective new methods are required to identify and monitor muscle pathology. Magnetic resonance imaging offers multiple potential biomarkers of disease severity in the muscular dystrophies. This Review uses a pathology-based approach to examine the ways in which MRI and spectroscopy have been used to study muscular dystrophies. Methods that have been used to quantitate intramuscular fat, edema, fiber orientation, metabolism, fibrosis, and vascular perfusion are examined, and this Review describes how MRI can help diagnose these conditions and improve upon existing muscle biomarkers by detecting small increments of disease-related change. Important challenges in the implementation of imaging biomarkers, such as standardization of protocols and validating imaging measurements with respect to clinical outcomes, are also described.