Preliminary data from the analysis of neuroradiological findings in Type I Alexander Disease

INTRODUCTION: Brain Magnetic Resonance Imaging (MRI) pattern recognition is crucial for guiding diagnostic process in Alexander Disease (AxD). Prior to identification of the causative genetic etiology for AxD, Van der Knaap et al. defined five MRI criteria for the neuroimaging-based diagnosis of type I AxD.1 We recently published and validated a disease evolution classification model, that subdivided type I AxD into type Ia, Ib, Ic, Id based on the disease progression.2 No study has been published to date that explores the correlation between MRI findings in AxD type I and the clinical evolution. Our aim was to investigate the presence of specific MRI features that are predictive of disease evolution over time. METHODS: Patients with genetically confirmed type I AxD were identified from several Leukodystrophy Centers worldwide. Clinical data were abstracted from the medical records and collected via RedCap, including age at symptoms onset, age at diagnosis, first symptoms, developmental milestones, and molecular confirmation. The patients were retrospectively classified based on the disease evolution classification. An MRI evaluating protocol was created adapting to AxD existing MRI scoring systems defined for other leukodystrophies, and brain MRIs were collected as deidentified files at each institution. First available MRI for each patient was analyzed by pediatric neuroradiology and leukodystrophy experts, and the imaging findings were correlated to the subjects’ disease subtypes. Fisher’s exact test was used to examine the significance of the association between the findings. RESULTS: 48 patients were enrolled. Mean age at onset was 0.96 y (range: 0.08 y – 1.83 y), mean age at first available MRI 3.36 y (range: 0.1 – 9.2 y). 1 patient was classified as type I a, 11 as type I b, 7 as type I c, 7 as type I d, 10 as type Ic/Id and 6 were too young to be classified (Undetermined). Involvement of subcortical frontal WM, deep and subcortical Parietal WM, deep and periventricular Occipital and Temporal WM, genu of corpus callosum and hilar region of cerebellum were able to differentiate between Ib and Id patients (p <0.05). Basal ganglia and medulla oblongata involvement was common across all subtypes and didn’t correlate with disease evolution trajectories. CONCLUSIONS: Our preliminary data suggest that there seem to be some early MRI features that are able to predict the eventual acquisition of autonomous ambulation in Type I AxD population. Further studies will be needed to validate these findings.