Multi‐scale network regression for brain‐phenotype associations

Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051) , we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.     

Thoracic splenosis: Case report of a symptomatic case

Thoracic splenosis is the autotransplantation of splenic tissue in the left thoracic cavity as a result of a splenic injury. This rare pathology is usually asymptomatic and may be discovered on incidental imaging, but the diagnosis often requires invasive procedures such as surgery in order to eliminate a neoplasic origin. We report a rare symptomatic case of a 39-year-old man presenting with chest pain and multiple nodules revealed on a computed tomography scan. The patient underwent a surgical exploration and the pathological studies concluded to a thoracic splenosis. Indeed, the previous medical history of the patient revealed a left thoraco-abdominal traumatism during childhood. The aim of this paper is to emphasize that the diagnosis can now be performed using only imaging techniques such as technetium-99 sulfur colloid or labelled heat-denatured red blood cell scintigraphy to avoid unnecessary invasive procedures including thoracotomy.     

Guidelines on clinical presentation and management of nondystrophic myotonias

The nondystrophic myotonias are rare muscle hyperexcitability disorders caused by gain-of-function mutations in the SCN4A gene or loss-of-function mutations in the CLCN1 gene. Clinically, they are characterized by myotonia, defined as delayed muscle relaxation after voluntary contraction, which leads to symptoms of muscle stiffness, pain, fatigue, and weakness. Diagnosis is based on history and examination findings, the presence of electrical myotonia on electromyography, and genetic confirmation. In the absence of genetic confirmation, the diagnosis is supported by detailed electrophysiological testing, exclusion of other related disorders, and analysis of a variant of uncertain significance if present. Symptomatic treatment with a sodium channel blocker, such as mexiletine, is usually the first step in management, as well as educating patients about potential anesthetic complications.