Background: Clinical trials for rare diseases pose unique challenges warranting alternative approaches in demonstrating treatment efficacy. Such trials face challenges including small patient populations, variable onset of symptoms and rate of disease progression, and ethical considerations, particularly in neurodegenerative diseases. In this study, we present the retrospective clinical global impression (RCGI) severity and change (RCGI-S/C) scale on 27 patients with GM1 gangliosidosis, a post hoc clinician-rated outcome measure to evaluate natural history study participants as historical controls for comparisons with treated patients in a clinical trial. Methods: We conducted a systematic chart review of 27 GM1 gangliosidosis natural history participants across 95 total visits. RCGI-S was assessed at the first visit and rated 1 (normal) to 7 (among the most extremely ill). Each subsequent follow-up was rated on the RCGI-C scale from 1 (very much improved) to 7 (very much worse). We demonstrate scoring guidelines of both scales with examples and justifications for this pilot in GM1 gangliosidosis natural history participants. The convergent validity of the RCGI scales was explored through correlations with magnetic resonance imaging (MRI) and the Vineland Adaptive Behavioral Scales. Results: We found strong association between the RCGI-S scores with gray matter volume (r(14) = −0.81; 95% CI [−0.93, −0.51], p < 0.001), and RCGI-C scores significantly correlated with increases in ventricular volume (χ²(1) = 18.6, p < 0.001). Baseline RCGI-S scores also strongly correlated with Vineland adaptive behavioral composite scores taken at the same visit (r(14) = −0.72; 95% CI [−0.93, −0.17], p = 0.02). Conclusion: RCGI-S/C scales, which use the clinical evaluation to assess the severity of disease of each patient visit over time, were consolidated into a single quantitative metric in this study. Longitudinal RCGI-C scores allowed us to quantify disease progression in our late-infantile and juvenile GM1 patients. We suggest that the retrospective CGI may be an important tool in evaluating historical data for comparison with changes in disease progression/mitigation following therapeutic interventions.

Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls. Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared the results for seven brain structures, including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum. Results: We found volBrain’s vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’s recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’s vol2Brain and SimNIBS’ headreco to have the strongest correlations, depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL’s FIRST to give the strongest correlations, depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable. Conclusions: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study, we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process that includes the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

GM2 gangliosidosis is lysosomal storage disorder caused by deficiency of the heterodimeric enzyme β-hexosaminidase A. Tay–Sachs disease is caused by variants in HEXA encoding the α-subunit and Sandhoff disease is caused by variants in HEXB encoding the β-subunit. Due to shared clinical and biochemical findings, the two have been considered indistinguishable. We applied T1-weighted volumetric analysis, diffusion tensor imaging (DTI), and correlational fiber tractography to assess phenotypic differences in these two diseases. 51 T1-weighted and 40 DTI scans from 19 Late-Onset GM2 patients with either late-onset Sandhoff disease (LOSD), or late-onset Tay–Sachs (LOTS) were included and compared to 1033 neurotypical control volumetric MRI scans. LOTS patients had significantly smaller cerebellum volume compared to neurotypical controls (p < 0.0001) and LOSD patients (p < 0.0001). There was no statistical difference for the volume of any structure between LOSD and neurotypical controls. DTI analysis showed LOTS patients had higher mean diffusivity (MD) in the left cerebellum (p = 0.003703), right cerebellum (p = 0.003435), superior cerebellar peduncle (p = 0.007332), and vermis (p = 0.01007) compared to LOSD. LOTS patients had lower fractional anisotropy (FA) in the left cerebellum (p = 0.005537), right cerebellum (p = 0.01905), SCP (p = 0.02844), and vermis (p = 0.02469) when compared to LOSD. Correlational fiber tractography identified fiber tracts in cerebellar pathways with higher FA and lower MD in LOSD patients compared to LOTS patients. Our study shows neurobiologic differences between these two related disorders. To our knowledge, this is the first study using correlational tractography in a lysosomal storage disorder. This result indicates a greater burden of cerebellar pathology in LOTS patients compared with LOSD patients.

Purpose: Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic condition associated with cutaneous and neoplastic manifestations and other physical manifestations, as well as cognitive, psychosocial, and behavioural difficulties. NF1 negatively impacts the health-related quality of life (HRQoL) of children. There is limited evidence regarding the determinants of HRQoL of children with NF1. The aim of this study was to (i) compare the HRQoL of children with NF1 and cognitive impairments to published data of healthy children and children with cancer and (ii) identify specific determinants of child and parent-proxy reports of psychosocial HRQoL. Methods: Children with NF1 and cognitive impairments (n = 135, 8–15 years 11 months) and their parents completed standardized measures assessing children’s HRQoL, behavioral and emotional functioning. Children completed a brief intelligence test. Correlations and multiple linear regressions were conducted to identify determinants of psychosocial HRQoL. Results: Children with NF1 had significantly poorer HRQoL for all domains than published data of healthy children and significantly poorer HRQoL for Psychosocial Health, School and Social Functioning than published data of children with cancer. For child self-report, attention problems and increased social stress predicted their psychosocial HRQoL. For parent-proxy reports, activities of daily living and depression were significant predictors of children’s psychosocial HRQoL. Social stress and depression were the strongest predictors of Psychosocial HRQoL. Conclusion: Routine screening and early identification of depressive symptoms and interventions that promote social support, coping and resiliency may improve the HRQoL of children with NF1.