Jean M. Johnston

GM1 gangliosidosis is an inherited, progressive, and fatal neurodegenerative lysosomal storage disorder with no approved treatment. We calculated a predicted brain ages and Brain Structures Age Gap Estimation (BSAGE) for 81 MRI scans from 41 Type II GM1 gangliosidosis patients and 897 MRI scans from 556 neurotypical controls (NC) utilizing BrainStructuresAges , a machine learning MRI analysis pipeline. NC showed whole brain aging at a rate of 0.83 per chronological year compared with 1.57 in juvenile GM1 patients and 12.25 in late-infantile GM1 patients, accurately reflecting the clinical trajectories of the two disease subtypes. Accelerated and distinct brain aging was also observed throughout midbrain structures including the thalamus and caudate nucleus, hindbrain structures including the cerebellum and brainstem, and the ventricles in juvenile and late-infantile GM1 patients compared to NC. Predicted brain age and BSAGE both correlated with cross-sectional and longitudinal clinical assessments, indicating their importance as a surrogate neuroimaging outcome measures for clinical trials in GM1 gangliosidosis.

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.

Late-onset Tay-Sachs (LOTS) disease and late-onset Sandhoff disease (LOSD) have long been considered indistinguishable due to similar clinical presentations and shared biochemical deficits. However, recent magnetic resonance imaging (MRI) studies have shown distinct cerebellar atrophy associated with LOTS. In this study, we furthered this investigation to determine if the cerebellar atrophy is globally uniform or preferentially targets certain cerebellar regions. We utilized DeepCERES , a deep learning cerebellar specific segmentation and cortical thickness pipeline to analyze differences between LOTS (n=20), LOSD (n=5), and neurotypical controls (n=1038). LOTS had smaller volumes of the whole cerebellum as well as cerebellar lobules IV, V, VI, VIIB, VIIIA, VIIIB, IX, and both Crus I and II compared to both LOSD and neurotypical controls. LOTS patients also had smaller cortical thickness of cerebellar lobules V, VI, VIIB, VIIIA, VIIIB, and both Crus I and II compared to both LOSD and neurotypical controls. Cerebellar functional and lesion localization studies have implicated lobules V and VI in speech articulation and execution while lobules VI, Crus I, VIIA, among others, have been implicated in a variety of behaviors and neuropsychiatric symptoms. Our observations provide a possible anatomical substrate to the higher prevalence of dysarthria and psychosis in our LOTS but not LOSD patients. Future studies are needed for direct comparisons considering phenotypic aspects such as age of symptom onset, presence and severity of dysarthria and ataxia, full characterization of neuropsychiatric profiles, molecular pathology and biochemical differences to fully understand the dichotomy observed in these two diseases.

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.

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 5 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 results for 7 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. Conclusion: 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 including 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 diffusion tensor imaging (DTI) and correlational fiber tractography to assess phenotypic differences in these two diseases. 40 DTI scans from 16 Late-Onset GM2 patients ( NCT00029965 ) with either Sandhoff (n = 4), or Tay-Sachs (n = 12) disease. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and quantitative anisotropy (QA) were calculated in fiber tracts throughout the whole brain, arcuate fasciculus, corpus callosum, and cerebellum. Correlational tractography was also performed to identify fiber tracts with group wide differences in DTI metrics between Tay-Sachs and Sandhoff patients. A linear mixed effects model was used to analyze the differences between Tay-Sachs and Sandhoff patients. Tay-Sachs patients had higher MD in the left cerebellum ( p = 0.003703), right cerebellum ( p = 0.003435), superior cerebellar peduncle (SCP, p = 0.007332), and vermis ( p = 0.01007). Sandhoff patients had higher FA in the left cerebellum ( p = 0.005537), right cerebellum ( p = 0.01905), SCP ( p = 0.02844), and vermis ( p = 0.02469). Correlational fiber tractography identified fiber tracts almost exclusively in cerebellar pathways with higher FA and QA, and lower MD, AD, and RD in Sandhoff patients compared to Tay-Sachs patients. Our study shows neurobiological differences between these two related disorders. To our knowledge, this is the first study using correlational tractography in a lysosomal storage disorder demonstrating these differences. This result indicates a greater burden of cerebellar pathology in Tay-Sachs patients compared with Sandoff patients.

GM1 gangliosidosis is an ultra-rare inherited neurodegenerative lysosomal storage disorder caused by biallelic mutations in the GLB1 gene. GM1 is uniformly fatal and has no approved therapies, although clinical trials investigating gene therapy as a potential treatment for this condition are underway. Novel outcome measures or biomarkers demonstrating the longitudinal effects of GM1 and potential recovery due to therapeutic intervention are urgently needed to establish efficacy of potential therapeutics. One promising tool is differential tractography, a novel imaging modality utilizing serial diffusion weighted imaging (DWI) to quantify longitudinal changes in white matter microstructure. In this study, we present the novel use of differential tractography in quantifying the progression of GM1 alongside age-matched neurotypical controls. We analyzed 113 DWI scans from 16 GM1 patients and 32 age-matched neurotypical controls to investigate longitudinal changes in white matter pathology. GM1 patients showed white matter degradation evident by both the number and size of fiber tract loss. In contrast, neurotypical controls showed longitudinal white matter improvements as evident by both the number and size of fiber tract growth. We also corroborated these findings by documenting significant correlations between cognitive global impression (CGI) scores of clinical presentations and our differential tractography derived metrics in our GM1 cohort. Specifically, GM1 patients who lost more neuronal fiber tracts also had a worse clinical presentation. This result demonstrates the importance of differential tractography as an important biomarker for disease progression in GM1 patients with potential extension to other neurodegenerative diseases and therapeutic intervention.

Purpose: GM1 gangliosidosis (GM1) is an ultra-rare lysosomal storage disease caused by pathogenic variants in galactosidase beta 1 ( GLB1 ; NM_000404), primarily characterized by neurodegeneration, often in children. There are no approved treatments for GM1, but clinical trials using gene therapy ( NCT03952637 , NCT04713475 ) and small molecule substrate inhibitors ( NCT04221451 ) are ongoing. Understanding the natural history of GM1 is essential for timely diagnosis, facilitating better supportive care, and contextualizing the results of therapeutic trials. Methods: Forty-one individuals with type II GM1 (n=17 late infantile and n=24 juvenile onset) participated in a single-site prospective observational study. Here, we describe the results of extensive multisystem assessment batteries, including clinical labs, neuroimaging, physiological exams, and behavioral assessments. Results: Classification of 37 distinct variants in this cohort was performed according to ACMG criteria and resulted in the upgrade of six and the submission of four new variants to pathogenic or likely pathogenic. In contrast to type I infantile, children with type II disease exhibited normal or near normal hearing and did not have cherry red maculae or significant hepatosplenomegaly. Some older children with juvenile onset developed thickened aortic and/or mitral valves with regurgitation. Serial MRIs demonstrated progressive brain atrophy that were more pronounced in those with late infantile onset. MR spectroscopy showed worsening elevation of myo-inositol and deficit of N -acetyl aspartate that were strongly correlated with scores on the Vineland Adaptive Behavior Scale and progress more rapidly in late infantile than juvenile onset disease. Conclusion: The comprehensive serial phenotyping of type II GM1 patients expands the understanding of disease progression and clarifies some common misconceptions about type II patients. Findings from this 10-year endeavor are a pivotal step toward more timely diagnosis and better supportive care for patients. The wealth of data amassed through this effort will serve as a robust comparator for ongoing and future therapeutic trials.