A French study has found that abnormalities in the fibers connecting different brain areas may contribute to muscle disorders, such as writer's cramp.
The research, published in the April 2009 issue of Archives of Neurology, studied 26 right-handed patients with writer's cramp and 26 right-handed control participants of the same sex and age without writer's cramp.
All subjects received diffusion-tensor MRI (DT-MRI), which has been shown to assess the status of coated nerve fibers that allow impulses to travel through the brain, also known as white matter.
The DT-MRI scans of patients with writer's cramp revealed areas of abnormality in the white matter of nerve pathways connecting the main sensorimotor cortex to brain areas below the cortex, such as the thalamus. The same abnormalities were not observed in healthy controls.
Lead author Dr. Christine Delmaire of the Centre Hospitalier Régional Universitaire Roger Salengro in Lille, France, and Institut National de la Santé et de la Recherche Médicale, Paris, noted that the results suggest that writer's cramp is associated with microstructural changes involving fibers that carry information from senses to the brain and motor information from the brain to the muscles to the primary sensorimotor cortex.
Authors added that it is unknown how these changes relate to the physiopathology of the disease.
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
