Different patterns of structural network impairments in two amyotrophic lateral sclerosis subtypes driven by 18F-FDG PET/MR hybrid imaging

The structural network damages in amyotrophic lateral sclerosis (ALS) patients are evident but contradictory due to the high heterogeneity of disease. We hypothesized that patterns of structural network impairments would be different in ALS subtypes by a data-driven method using 18F-FDG PET/MR hybrid imaging. 50 patients with ALS and 23 healthy controls (HCs) were collected PET, structural MRI and diffusion tensor imaging data by a 18F-FDG PET/MR hybrid. Two ALS subtypes were identified as the optimal cluster based on gray matter volume and standardized uptake value ratio. Network metrics at the global, local and connection levels were compared to explore the impaired patterns of structural network in the identified subtypes. Compared with HCs, the two ALS subtypes displayed a pattern of a locally impaired structural network centralized in the sensorimotor network and a pattern of an extensively impaired structural network in the whole brain. When comparing the two ALS subgroups by a support vector machine classifier based on the decreases in nodal efficiency of structural network, the individualized network scores were obtained in every ALS patient and demonstrated a positive correlation with disease severity. We clustered two ALS subtypes by a data-driven method, which encompassed different patterns of structural network impairments. Our results imply that ALS may possess the intrinsic damaged pattern of white matter network and thus provide a latent direction for stratification in clinical research.