The aim of this study was to characterize brain metabolic abnormalities in patients with macrophagic myofascitis (MMF) and the relationship with cognitive dysfunction through the use of PET with 18F-FDG. Methods:18F-FDG PET brain imaging and a comprehensive battery of neuropsychological tests were performed in 100 consecutive MMF patients (age [mean ± SD], 45.9 ± 12 y; 74% women). Images were analyzed with statistical parametric mapping (SPM12). Through the use of analysis of covariance, all 18F-FDG PET brain images of MMF patients were compared with those of a reference population of 44 healthy subjects similar in age (45.4 ± 16 y; P = 0.87) and sex (73% women; P = 0.88). The neuropsychological assessment identified 4 categories of patients: those with no significant cognitive impairment (n = 42), those with frontal subcortical (FSC) dysfunction (n = 29), those with Papez circuit dysfunction (n = 22), and those with callosal disconnection (n = 7). Results: In comparison with healthy subjects, the whole population of patients with MMF exhibited a spatial pattern of cerebral glucose hypometabolism (P < 0.001) involving the occipital lobes, temporal lobes, limbic system, cerebellum, and frontoparietal cortices, as shown by analysis of covariance. The subgroup of patients with FSC dysfunction exhibited a larger extent of involved areas (35,223 voxels vs. 13,680 voxels in the subgroup with Papez circuit dysfunction and 5,453 voxels in patients without cognitive impairment). Nonsignificant results were obtained for the last subgroup because of its small population size. Conclusion: Our study identified a peculiar spatial pattern of cerebral glucose hypometabolism that was most marked in MMF patients with FSC dysfunction. Further studies are needed to determine whether this pattern could represent a diagnostic biomarker of MMF in patients with chronic fatigue syndrome and cognitive dysfunction.
Keywords: PET/CT; aluminum hydroxide; brain 18F-FDG PET; macrophagic myofascitis; neurology; statistical parametric mapping.
© 2017 by the Society of Nuclear Medicine and Molecular Imaging.