Fatigue is a major determinant of quality of life and motor function in patients affected by several neuromuscular diseases, each of them characterized by a peculiar physiopathology and the involvement of numerous interplaying factors. This narrative review aims to provide an overview on the pathophysiology of fatigue at a biochemical and molecular level with regard to muscular dystrophies, metabolic myopathies, and primary mitochondrial disorders with a focus on mitochondrial myopathies and spinal muscular atrophy, which, although fulfilling the definition of rare diseases, as a group represent a representative ensemble of neuromuscular disorders that the neurologist may encounter in clinical practice. The current use of clinical and instrumental tools for fatigue assessment, and their significance, is discussed. A summary of therapeutic approaches to address fatigue, encompassing pharmacological treatment and physical exercise, is also overviewed.

Metabolic myopathies are a group of genetic disorders that affect the normal functioning of muscles due to abnormalities in metabolic pathways. These conditions result in impaired energy production and utilization within muscle cells, leading to limitations in muscle function with concomitant occurrence of related signs and symptoms, among which fatigue is one of the most frequently reported. Understanding the underlying molecular mechanisms of muscle fatigue in these conditions is challenging for the development of an effective diagnostic and prognostic approach to test targeted therapeutic interventions. This paper outlines the key biomolecules involved in muscle fatigue in metabolic myopathies, including energy substrates, enzymes, ion channels, and signaling molecules. Potential future research directions in this field are also discussed.

We aim to develop a deep learning-based algorithm for automated segmentation of thigh muscles and subcutaneous adipose tissue (SAT) from T1-weighted muscle MRIs from patients affected by muscular dystrophies (MDs). From March 2019 to February 2022, adult and pediatric patients affected by MDs were enrolled from Azienda Ospedaliera Universitaria Pisana, Pisa, Italy (Institution 1) and the IRCCS Stella Maris Foundation, Calambrone-Pisa, Italy (Institution 2), respectively. All patients underwent a bilateral thighs MRI including an axial T1 weighted in- and out-of-phase (dual-echo). Both muscles and SAT were manually and separately segmented on out-of-phase image sets by a radiologist with 6 years of experience in musculoskeletal imaging. A U-Net1 and U-Net3 were built to automatically segment the SAT, all the thigh muscles together and the three muscular compartments separately. The dataset was randomly split into the on train, validation, and test set. The segmentation performance was assessed through the Dice similarity coefficient (DSC). The final cohort included 23 patients. The estimated DSC for U-Net1 was 96.8%, 95.3%, and 95.6% on train, validation, and test set, respectively, while the estimated accuracy for U-Net3 was 94.1%, 92.9%, and 93.9%. Both of the U-Nets achieved a median DSC of 0.95 for SAT segmentation. The U-Net1 and the U-Net3 achieved an optimal agreement with manual segmentation for the automatic segmentation. The so-developed neural networks have the potential to automatically segment thigh muscles and SAT in patients affected by MDs.