{"paper_id":"38d1cbda-47d0-482e-8722-10a23a271791","body_text":"GM1 Differential Tractography \n 1 \nDifferential Tractography: A Biomarker for Neuronal Function in \nNeurodegenerative Disease \n \n• Connor J. Lewis, connor.lewis@nih.gov, Office of the Clinical Director and Medical Genetics \nBranch, National Human Genome Research Institute, 10 Center Drive, Bethesda MD USA \n• Zeynep Vardar, zeynep.vardar@umassmed.edu, Department of Radiology, University of \nMassachusetts Chan Medical School, 55 N Lake Ave, Worcester MA USA \n• Anna Luisa Kühn, anna.kuhn@umassmemorial.org, Department of Radiology, University of \nMassachusetts Chan Medical School, 55 N Lake Ave, Worcester MA USA \n• Jean M. Johnston , jean.johnston@nih.gov, Office of the Clinical Director and Medical \nGenetics Branch, National Human Genome Research Institute, 10 Center Drive, Bethesda MD \nUSA \n• Precilla D’Souza , precilla.d’souza@nih.gov, Office of the Clinical Director and Medical \nGenetics Branch, National Human Genome Research Institute, 10 Center Drive, Bethesda MD \nUSA \n• William A. Gahl, gahlw@mail.nih.gov, Medical Genetics Branch, National Human Genome \nResearch Institute, 10 Center Drive, Bethesda MD USA \n• Mohammed Salman Shazeeb , mohammed.shazeeb@umassmed.edu, Department of \nRadiology, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester MA \nUSA \n• Cynthia J. Tifft, cynthiat@mail.nih.gov, Office of the Clinical Director and Medical Genetics \nBranch, National Human Genome Research Institute, 10 Center Drive, Bethesda MD USA \n• Maria T. Acosta, acostam@nhgri.nih.gov, Office of the Clinical Director and Medical Genetics \nBranch, National Human Genome Research Institute, 10 Center Drive, Bethesda MD USA \n \nCorresponding Author: Maria T. Acosta; (301) 451-2451, acostam@nhgri.nih.gov \n \nKeywords: GM1 Gangliosidosis, Differential Tractography, Imaging Biomarkers \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nGM1 Differential Tractography \n 2 \nAbstract \nGM1 gangliosidosis is an ultra-rare inherited neurodegenerative lysosomal storage disorder \ncaused by biallelic mutations in the GLB1 gene. GM1 is uniformly fatal and has no approved \ntherapies, although clinical trials investigating gene therapy as a potential treatment for this \ncondition are underway. Novel outcome measures or biomarkers demonstrating the longitudinal \neffects of GM1 and potential recovery due to therapeutic intervention are urgently needed to \nestablish efficacy of potential therapeutics. One promising tool is differential tractography, a \nnovel imaging modality utilizing serial diffusion weighted imaging (DWI) to quantify \nlongitudinal changes in white matter microstructure. In this study, we present the novel use of \ndifferential tractography in quantifying the progression of GM1 alongside age-matched \nneurotypical controls. We analyzed 113 DWI scans from 16 GM1 patients and 32 age-matched \nneurotypical controls to investigate longitudinal changes in white matter pathology. GM1 \npatients showed white matter degradation evident by both the number and size of fiber tract loss. \nIn contrast, neurotypical controls showed longitudinal white matter improvements as evident by \nboth the number and size of fiber tract growth. We also corroborated these findings by \ndocumenting significant correlations between cognitive global impression (CGI) scores of \nclinical presentations and our differential tractography derived metrics in our GM1 cohort. \nSpecifically, GM1 patients who lost more neuronal fiber tracts also had a worse clinical \npresentation. This result demonstrates the importance of differential tractography as an important \nbiomarker for disease progression in GM1 patients with potential extension to other \nneurodegenerative diseases and therapeutic intervention. \n \nIntroduction  \nDiffusion weighed imaging (DWI) is a magnetic resonance imaging ( MRI) technique utilizing \nmultiple radio frequency pulses to evaluate water diffusion in vivo 1. DWI approaches have  \nexpanded since the evolution of echo -planar imaging technique s, allowing for faster acquisition \ntimes2,3. DWI has proven to be useful in classifying cancer cells, ischemia, white matter diseases, \nand other conditions based on the extent of visualized local diffusion restriction3-6. \n \nDiffusion tensor imaging (DTI) builds upon DWI by quantifying water’s local diffusion (isotropy) \nand diffusion restriction (anisotropy) utilizing the diffusion tensor matrix 7,8. This quantification \nhas given rise to classical DTI metrics, including fractional anisotropy (FA), mean diffusivity \n(MD), axial diffusivity (AD), and radial diffusivity (RD); these techniques assess axonal \nmyelination and structural changes in the brain9-11. DTI has also allowed for the inception of fiber \ntractography, with the ability to map white matter neuronal pathways12-14.  \n \nDifferential tractography is a new technique utilizing multiple longitudinal D TI scans with the \ncapability of mapping alterations to specific white matter -derived neuronal pathways  over \ntime15,16. A classical DTI measure (FA/MD/AD/RD) is calculated for each fiber tract at baseline \nand follow-up timepoints, and the change is evaluated against a pre-determined threshold15. Fiber \ntracts exceeding that threshold are considered a growth or a loss depending on time orientation15. \n \nPrevious investigations utilizing differential tractography have been limited to adults with neuronal \ninjury, with specific foci on Huntington’s disease16, multiple sclerosis15, end stage renal disease17, \nand traumatic brain injury 18, demonstrating temporal changes associated with the degenerative \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 3 \nprogression of these conditions.  To the best of our knowledge, no studies have been performed in \nchildren. \n \nGM1 gangliosidosis, is an ultra-rare pan-ethnic degenerative neurological disease affecting 1 in \n100,000-200,000 births19. GM1 gangliosidosis is caused by biallelic, loss-of-function mutations in \nGLB1, which encodes lysosomal β-galactosidase20. Decreased β-galactosidase activity results in \nthe toxic accumulation of GM1 ganglioside and GA1 glycolipid, primarily in the central nervous \nsystem where the rate of synthesis of these molecules is highest21,22. GM1 clinically manifests as \nthree types based on age at symptom onset and rate of disease progression20. GM1 Type I (infantile) \nhas symptom onset in the first six months of life with rapid progression and death at 2 -3 years23. \nGM1 Type II is divided into two subtypes, i.e., late -infantile with symptom onset between seven \nand twenty-four months and death in the second decade and juvenile , with onset at 3-5 years and \nsurvival into the 3rd or 4th decade20,24. GM1 Type III patients generally have symptom onset in the \nsecond decade and more gradual progression and clinical variability with long-term survival22,25.  \n \nThere are no approved therapies for GM1 gangliosidosis and the disease is uniformly fatal 26. \nHowever, adeno-associated virus (AA V) gene therapy techniques have been advancing rapidly and \nmay be applicable to GM1 disease27-30. Objective outcome measures will be required to assess the \nefficacy of therapeutic interventions, such as AA V-mediated gene therapy, in GM1. DWI, whose \nuse in GM1 has been limited to one case report that found hypo-intensities in the globus pallidum31, \nmay fulfill this requirement. In t his study, we present the first use of differential tractography to \nassess neuronal degeneration in children and adults with GM1 gangliosidosis compared with age-\nmatched neurotypical developmental controls. The findings may be applicable as outcome imaging \nparameters to assess the efficacy of therapeutic interventions such as gene therapy. \n \nMethods \nThe Natural History of GM1 Gangliosidosis \nTo determine the natural progression of GM1 gangliosidosis, participants from the NHGRI study, \nthe “Natural History of Glycosphingolipid & Glycoprotein Storage Disorders” with a diagnosis of \nType II GM1 diagnosis and repeated DWI were included in this analysis as the GM1 gangliosidosis \ncohort (NCT00029965)32. Ten GM1 natural history study (NHS) participants were included in the \nage-matched comparisons, and 16 GM1 NHS participants were included in the longitudinal \nanalysis (see supplement A). \n \nNormal Controls \nTo determine how the GM1 cohort developed in relation to normal children, age-matched normal \ncontrols were included from two open-source data repositories. The OpenScienceFramework and \nOpenNeuro include the “Calgary Preschool magnetic resonance imaging (MRI) dataset”33 (n = \n13) with participants aged 2-8 years and the “Queensland Twin Adolescent Brain (QTAB)”34 (n = \n19) includes participants aged 9-16 years. Participants were selected for inclusion in this study \nby matching baseline age and latest follow up with the youngest GM1 patients (Figure 1).  \n \nCGI Scores \nCognitive Global Impression (CGI) is a clinician rating scale used to assess initial global illness \nseverity (CGI-S) and overall change (CGI-C) from baseline with specific interventions35. It is \nextensively used in clinical trials and can be administered to a wide variety of patient \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 4 \npopulations. We retrospectively used CGI to assess our NHS patients at the beginning of the \nstudy (CGI-S) and longitudinally (CGI-C) during their participation in the study36,37. \n \nDWI Acquisition \nNatural History Study Patients \nNHS patients were sedated with propofol and/or sevoflurane for the duration of the scanning \nprotocol. A Philips Achieva 3T system equipped with an 8-channel SENSE head coil was used to \nscan all NHS patients. DTI images were acquired with the following parameters: \nTR/TE=6400/100 ms, 32-gradient encoding directions, b-values=0 and 1000 s/mm2, voxel \nsize=1.875mm×1.875mm×2.5mm, slice thickness=2.5 mm, acquisition matrix=128×128, \nNEX=1, FOV=24 cm. \n \nCalgary Normal Controls (NC)33 \nA General Electric 3T MR750w system with a 32-channel head coil was used for scanning all \nCalgary normal controls using a single shot spin echo-planar imaging sequence. DTI images \nwere acquired with the following parameters for Calgary normal controls: TR/TE=6750/79 ms, \n30-gradient encoding directions, b-values=0 and 750 s/mm2, voxel size=1.6mm×1.6mm×2.2mm, \nslice thickness=2.2 mm, FOV=20 cm.  \n \nQueensland Normal Controls (NC)34 \nA 3T Magnetom Prisma (Siemens Medical Solutions, Erlangen) and a 64-channel head coil at the \nCentre for Advanced Imaging, University of Queensland employed a multi-shell with an \nanterior-posterior phase encoding direction. DTI images were acquired with the following \nparameters for Queensland normal controls: TR/TE= 3800/70 ms, 23-gradient encoding \ndirections, b-values=0, 1,000, and 3,000 s/mm2, voxel size=2mm×2mm×2mm, slice thickness=2 \nmm, FOV=24 cm. \n \nDWI Processing \nAll DWI was preprocessed for artifacts, eddy currents, motion, and susceptibility induced \ndistortions using MRtrix3’s (MRtrix, v3.0.4)38 dwifslpreproc39-41 command utilizing the \ndwi2mask42 function followed by FSL’s (FSL, v6.0.5) eddy40 and topup40,41 functions. \nPreprocessed data were imported into DSI Studio (DSI Studio, v2023), where imaging was \nquality checked for bad slices, a U-Net mask was created, and generalized q-sampling imaging \n(GQI) based reconstruction was performed with a diffusion sampling length ratio of 1.2543 (see \nsupplement B). \n \nDifferential Tractography \nWhole brain differential tractography was also performed in DSI Studio where fiber tract gains \nand losses were calculated using 10%, 20%, 30%, 40%, and 50% fractional anisotropy \nthresholds15. The angular threshold was 60 and the step size was 1 mm. Tracks < 20 mm or > 200 \nmm were discarded and 1,000,000 seeds were placed. Fiber tract gains were determined where \nthe difference in FA between the follow-up and the baseline image exceeded the threshold. Fiber \ntract losses were determined where the difference in FA between the baseline and the follow-up \nimage exceeded the threshold. Net fiber tract metrics were assessed as the difference between the \ngrowth and the loss. \n \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 5 \nTo account for DWI sequencing and MRI scanner differences between cohorts, whole brain \ntractography was performed with the same parameters as above on each participant’s baseline \ndiffusion weighted image. Percentage changes in fiber tract number and fiber tract volume were \ncalculated relative to each participant’s baseline whole brain tractography.  \n \nStatistical Analysis  \nStatistical analysis was performed in R studio (The R Foundation, v4.3.1). Between group \nanalyses were performed between age-matched cohorts to demonstrate the effects of GM1 in 5 \npatients and 30 normal controls using Welch’s t-test. Longitudinal statistical analysis was \nperformed between all participants who had serial DWI to demonstrate the viability of \ndifferential tractography as a biomarker and included 16 GM1 patients at 37 timepoints. Linear \nmixed effects modeling was used to evaluate longitudinal differential tractography parameters, \nthe net percentage change in fiber tract number, and the net percentage change in fiber tract \nvolume against CGI-C44. \n \nResults \nAge-matched Between Group Analysis \nThere were no significant differences in baseline age (t = 0.036, p = 0.9719), follow-up age (t = \n0.036, p = 0.9839), or follow-up interval (t = 0.181, p = 0.8599) between the age-matched \nNatural History cohort and the normal controls (Figure 1).  \n \nFigure 2 shows substantial differences in longitudinal fiber tract development  between GM1 \npatients and normal controls. At a low FA threshold (10%), the GM1 patients show drastic fiber \ntract losses (red)  throughout the entire brain. In this patient, at  a high FA threshold (50%) fiber \ntract losses are primarily located in the corpus callosum, a known location of abnormalities in \nGM1. In contrast, at a low FA threshold, the normal control shows significant fiber tract growth \nspread throughout the entire brain with minimal loss, while at a high FA (50%), there is milder and \nmore localized growth with minimal fiber tract loss. \n \nFiber Tractography Metrics \nNormal controls showed statistically significant growth of fiber tract number and volume when \ncompared to GM1 NHS patients at all FA thresholds (Figures 3&4). GM1 NHS patients showed \nstatistically significant fiber tract number and volume loss when compared to normal controls at \nall FA thresholds (Figures 3&4).  \n \nFigure 5 similarly demonstrates the longitudinal effects of GM1. GM1 patients show significant \nnet fiber tract losses in both density (number) and size of fiber tracts (volume). Normal controls \nshow growth in these domains associated with neurotypical development. \n \nLongitudinal Analysis \nFiber tract number growth did not correlate with a change in clinical presentation as assessed by \nCGI-C (𝜒2 = 3.246, p = 0.0716, R2=0.0818). Fiber tract number loss (𝜒2 = 17.22, p < 0.001, \nR2=0.3655) and net fiber tract number (𝜒2 = 18.31, p < 0.001, R2 = 0.3837, Figure 6) both \ncorrelated with CGI-C. Fiber tract volume growth did not influence CGI-C (𝜒2 = 3.821, p = \n0.0506, R2=0.0957). Fiber tract volume loss (𝜒2 = 23.01, p < 0.001, R2=0.4561) and net fiber \ntract volume (𝜒2 = 24.94, p < 0.001, R2 = 0.4833, Figure 6) both correlated with CGI-C.  \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 6 \n \nDiscussion \nIn this study, we objectively identified and longitudinally quantified changes in fiber tract count \nand volume in normal individuals and in patients with GM1 gangliosidosis using differential \ntractography. This is a novel neuroradiological tool that allows the assessment of neuronal fiber \ntrack changes over time. We could infer that the changes reflect aberrations in neuronal cell \ngrowth resulting from neurodevelopmental and/or neurodegenerative conditions compared with \nneurotypical controls.  \n \nPrevious investigations into differential tractography have been limited to adult degenerative \ndisorders15-18. Here, we combined a population of children and adults with the same \nneurodegenerative condition (GM1) and compared their results with those of neurotypical \nindividuals. This allowed for the assessment of not only the neurodegeneration associated with \nthe medical condition, but also the expected gains and losses in typical neurodevelopmental \ntrajectories. \n \nPrevious investigations into the white matter pathology of GM1 have demonstrated longitudinal \ndegradation and delays in myelination or white matter development45. Our investigation supports \nthese findings, since GM1 patients showed significant longitudinal neuronal cell loss with \nminimal growth in our age-matched analysis (Figure 2). Numerous studies46-48 have also \ndemonstrated the neurotypical development of white matter through increases in FA and \ndecreases in MD until reaching a peak at approximately 30 years of age. Our study also supports \nthese findings, since net fiber tract metrics derived from FA changes were shown to increase in \nour neurotypical controls (Figure 5).  \n \nTo better assess the importance and meaning of our neuroradiological findings related to brain \nstructure and function, we correlated these results with clinical assessments used in our \npopulation to prepare for an upcoming clinical trial. The CGI-C is a widely used scale in clinical \ntrials that allows the assessment of clinical changes associated with tested interventions. It is \ntraditionally used as a prospective assessment in addition to other outcome measurements \nselected for a specific study. In our case, we used a novel retrospective version of the CGI to \nassess disease progression over time from historical records37. In our longitudinal analysis of \ndifferential tractography, we found significant correlations between net differential tractography \nmetrics and clinical presentation assessed by CGI-C; increased fiber tract loss corresponded to a \nworsening in the clinical presentation (Figure 6). This demonstrates the utility of differential \ntractography as a biomarker in evaluating longitudinal change in the clinical presentation of \npatients with GM1 and potentially other neurodegenerative diseases.  \n \nTo determine the sensitivity of these results, we tested our fiber tract metrics at varying FA \nthresholds between 10 and 50 percent. Only fiber tracts with a change in FA exceeding the \nspecified threshold in both directions (growth and loss) were identified by differential \ntractography. We found significant differences in growth and loss of both the number of fiber \ntracts and the volume of these tracts at all FA thresholds between GM1 gangliosidosis patients \nand neurotypical controls. This suggests that our results are independent of the FA threshold \nbeing examined and demonstrates the utility of differential tractography in differentiating \nbetween neurotypical and neurodegenerative developmental white matter changes. While a 10% \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 7 \nFA threshold exhibited stronger results in terms of fiber tract growth and loss (Figure 3&4), it \nlikely also had more false discoveries as described in Yeh et al15. Our longitudinal analysis also \nsupported this notion; at all FA thresholds between 10% and 50%, we found statistically \nsignificant correlations between net fiber tract metrics and CGI-C scores (see supplement C). \nLower FA thresholds yielded stronger correlations with CGI-C; this requires further validation. \n \nSome of the limitations in our study need to be considered as we aim to use this methodology in \nclinical trials and other research projects that require identification of temporal changes in brain \nanatomy and structure. One limitation is the variability in scanning sequences among GM1 \npatients and normal controls. We think this issue was mitigated by using the baseline whole brain \ntractography, which allows for the relative percentage of fiber tract growth and loss for each \nparticipant to be calculated prior to inter-cohort comparisons. Similarly, the smaller number of \ndiffusion directions in the scanning sequences is a limitation of this study. However, while Yeh et \nal15. demonstrated the limitations associated with a reduced number of diffusion directions, they \nfound this limitation is associated with a smaller number of detections; this suggests that the use \nof an optimal scanning protocol could yield even more impressive differential tractography \nresults. Another limitation of this study is the absence of a sham sequence and subsequent \nanalysis of the false discovery rate15. GM1 NHS participants also underwent propofol sedation \nduring DWI acquisition where neurotypical controls remained awake during their DWI \nacquisition protocols. Previous studies have suggested that propofol does not influence DTI \nparameters49,50, but this requires further validation. Lastly, this study is limited by a small sample \nsize; future studies evaluating the role of differential tractography as a neurogenerative \nbiomarker should incorporate a larger sample size to demonstrate reliability. Nevertheless, we \nbelieve that this work represents an important step forward in the identification of biomarkers for \ndisease progression that considers the well-known but rarely included changes in brain structure \nand function associated with neurodevelopment and aging. \n \nConclusion \nThis study is the first to explore the utility of differential tractography in demonstrating \nlongitudinal changes in white matter fiber tracts resulting from neurodevelopment and \nneurodegeneration in GM1 gangliosidosis. Overall, GM1 patients showed statistically significant \nloss of white matter tract count and white matter tract volume, reflecting the natural progression \nof GM1. Differential tractography results strongly correlated with longitudinal clinical outcomes \nas measured by CGI-C in GM1 gangliosidosis patients. These results indicate the importance of \ndifferential tractography as a robust biomarker for disease progression in GM1 patients, and \npotentially extend to a similar role in other neurodegenerative diseases and lysosomal storage \ndisorders. \n \nData Availability  \nThe data described in this manuscript are available from the corresponding author upon \nreasonable request.  \n \n \n \n \n \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 8 \nAcknowledgments  \nWe thank the participants and their families for the generosity of their time and efforts. We are \nalso grateful to many staff members and care providers who contributed their expertise over the \nyears. Magnetic resonance imaging analysis in this work utilized the computational resources of \nthe Biowulf Linux cluster at the National Institutes of Health (http://hpc.nih.gov).  \n \nFunding Statement \nThis work was supported by the Intramural Research Program of the National Human Genome \nResearch Institute (Tifft ZIAHG200409). This report does not represent the official view of the \nNational Human Genome Research Institute (NHGRI), the National Institutes of Health (NIH), \nor any part of the US Federal Government. No official support or endorsement of this article by \nthe NHGRI or NIH is intended or should be inferred. Natural History Protocol: NCT00029965. \n \nAuthor Contributions \nConceptualization: CJL, MSS, MTA, CJT; Data Curation: PD, JMJ, CJT, MTA; Funding \nAcquisition: CJT; Methodology: CJL, ZV , MSS, CJT, MTA; Visualization: CJL, MTA; Writing-\noriginal draft: CJL, WAG, CJT, MTA; Writing-reviewing & editing; ZV , ALK, PD, JMJ, WAG, \nMSS, CJT, MTA \n \nEthics Declaration \nThe NIH Institutional Review Board approved this protocol (02-HG-0107). Informed consent was \ncompleted with parents or legal guardians of the patients. All participants were assessed for their \nability to provide assent; none were deemed capable. \n \nConflict of Interest Disclosure \nThe authors declare no conflict of interest. \n \nReferences \n1. Charles-Edwards EM, deSouza NM. Diffusion-weighted magnetic resonance imaging and its \napplication to cancer. Cancer Imaging. 2006 Sep 13;6(1):135 -43. doi: 10.1102/1470 -\n7330.2006.0021. PMID: 17015238; PMCID: PMC1693785. \n2. DeLaPaz R. L. (1994). 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It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 12 \nsystem myelination. Pediatr Dev Pathol. 2000 Jan -Feb;3(1):73-86. doi: \n10.1007/s100240050010. PMID: 10594135. \n46. Kochunov, P., Williamson, D. E., Lancaster, J., Fox, P., Cornell, J., Blangero, J., & Glahn, D. \nC. (2012). Fractional anisotropy of water diffusion in cerebral white matter across the lifespan. \nNeurobiology of aging, 33(1), 9–20. https://doi.org/10.1016/j.neurobiolaging.2010.01.014 \n47. Lebel, C., Gee, M., Camicioli, R., Wieler, M., Martin, W., & Beaulieu, C. (2012). Diffusion \ntensor imaging of white matter tract evolution over the lifespan. NeuroImage, 60(1), 340–352. \nhttps://doi.org/10.1016/j.neuroimage.2011.11.094 \n48. Lars T. Westlye, Kristine B. Walhovd, Anders M. Dale, Atle Bjørnerud, Paulina Due -\nTønnessen, Andreas Engvig, Håkon Grydeland, Christian K. Tamnes, Ylva Østby, Anders M. \nFjell, Life-Span Changes of the Human Brain White Matter: Diffusion Tensor Imaging (DTI) \nand V olumetry, Cerebral Cortex, V olume 20, Issue 9, September 2010, Pages 2055 –2068, \nhttps://doi.org/10.1093/cercor/bhp280 \n49. Cavaliere C, Aiello M, Di Perri C, Fernandez-Espejo D, Owen AM, Soddu A. Diffusion tensor \nimaging and white matter abnormalities in patients with disorders of consciousness. Front Hum \nNeurosci. 2015 Jan 6;8:1028. doi: 10.3389/fnhum.2014.01028. PMID: 256103 88; PMCID: \nPMC4285098. \n50. Sidaros, A., Engberg, A. W., Sidaros, K., Liptrot, M. G., Herning, M., Petersen, P., Paulson, O. \nB., Jernigan, T. L., & Rostrup, E. (2008). Diffusion tensor imaging during recovery from severe \ntraumatic brain injury and relation to clinical outcome: a longitudinal study. Brain : a journal \nof neurology, 131(Pt 2), 559–572. https://doi.org/10.1093/brain/awm294 \n \n \nFigure Legends \nFigure 1. Participant age at each scan with GM1 patients shown in red and normal controls \nshown in blue. Each DWI scan is represented as a circle for all 113 scans where each of the 48 \nparticipants is on a separate row.  \n \nFigure 2. Differential tractography assessed fiber tract gains (green) and losses (red) at varying \nFA thresholds for one age matched late-infantile GM1 patient and one age matched normal \ncontrol. At a low FA threshold (10%), the GM1 patient shows global and substantial fiber tract \nloss. At a high FA threshold (50%), the GM1 patients show milder fiber tract loss, localized \nprimarily to the corpus callosum as indicated by the arrows. The neurotypical control shows \nglobal and moderate fiber tract growth at a low FA threshold (10%) with milder fiber tract \ngrowth at a high FA threshold (50%). \n \nFigure 3. Age-matched differential tractography between group analysis of the number of fiber \ntracts. Row one indicates fiber tract growth as a percentage compared to baseline. Row two \nindicates fiber tract loss as a percentage compared to baseline. Row three indicates the net fiber \ntract number (growths minus losses) as a percentage compared to baseline. The columns indicate \nwhich fractional anisotropy threshold was tested from 10% to 50%. \n \nFigure 4. Age-matched differential tractography between group analysis of fiber tract volume. \nRow one indicates fiber tract volume increases as a percentage compared to baseline. Row two \nindicates fiber tract volume loss as a percentage compared to baseline. Row three indicates the \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 13 \nnet fiber tract volume (growth minus loss) as a percentage compared to baseline. The columns \nindicate which fractional anisotropy threshold was tested from 10% to 50%. \n \nFigure 5. Differential tractography longitudinal analysis. A.) Net fiber tract number against \nparticipant age. B.) Net fiber tract volume against participant age. \n \nFigure 6. Differential Tractography correlations of net fiber tract number and net fiber tract \nvolume with CGI-C change scores with GM1 patients at a 20% fractional anisotropy threshold. \n \nFigures \n \nFigure 1. \n \n \n \n \n \n \n \n \n \n \n0 10 20 30\n0\n20\n40\nAge(Years)\nParticipant ID\nQueensland\nNHS (Age Matched)\nCalgary\nNHS (Longitudinal \nAnalysis)\nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 14 \nFigure 2. \n \n \n \nFigure 3. \n \n \n \n \n \n \n \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 15 \nFigure 4. \n \n \n \n \nFigure 5. \nA.                                                                   B. \n     \n \n \n \n \n \n \n \n \n10 20 30\n-20\n-10\n0\n10\nFiber Tract Number (%)\nFiber Tract Development\nGM1 Patients\nAge (Years)\nNormal Controls\n10 20 30\n-20\n0\n20\nFiber Tract Volume (%)\nFiber Tract Development\nAge (Years)\nGM1 Patients\nNormal Controls\nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 16 \nFigure 6. \nA.                                                                   B. \n     \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n-25 -20 -15 -10 -5 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Number (%)\nCGI-C\nCGI-C: FA = 20%\nMuch \nimproved\nMuch \nworse\nMinimally\nimproved\nVery much\nworse\nMinimally \nworse\nNo \nChange\nR2 = 0.3837\np < 0.0001Very much\nimproved\n-40 -30 -20 -10 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Volume (%)\nCGI-C\nCGI-C:Volume - 20%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.4833\np < 0.0001\nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 17 \nDifferential Tractography: A Biomarker for Neuronal Function in Neurodegenerative Disease \nSupplementary Material \n \nTable of Contents \n \nMethods……………………………………………………………………………………….…18 \n \nSupplement A: Natural History Study Age-Matched Methodology……………..………………18 \n \nSupplement B: Diffusion Weighted Imaging (DWI) Sequence Parameters and Processing…….21 \n \nResults…………………………………………………………………………………………...23 \n \nSupplement C: Fractional Anisotropy Thresholds Correlate with CGI-C..………………….…..23 \n \nReferences..……………………………………………………………………………………...27 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 18 \nSupplementary Methods \n \nSupplement A: Natural History Study Age-Matched Methodology \n \nNHGRI Natural History Study (NCT00029965)1 \nStudy description \nThis is a natural history study that will evaluate any patient with enzyme- or DNA-confirmed \nGM1 or GM2 gangliosidosis, sialidosis or galactosialidosis. Patients may be evaluated every 6 \nmonths for infantile onset disease, yearly for juvenile onset and approximately every two years \nfor adult-onset disease as long as they are clinically stable to travel. Data will be evaluated \nserially for each patient and cross-sectionally for patients of similar ages and genotypes. \nGenotype-phenotype correlations will be made where possible although these are rare disorders \nand the majority of the patients are compound heterozygotes. \n \nObjectives \n- To study the natural history and progression of neurodegeneration in individuals with \nglycosphingolipid storage disorders (GSL), GM1 and GM2 gangliosidosis, and \nglycoprotein (GP) disorders including sialidosis and galactosialidosis using clinical \nevaluation of patients and patient/parent surveys. \n- To develop sensitive tools for monitoring disease progression. \n- To identify biological markers in blood, cerebrospinal fluid, and urine that correlate with \ndisease severity and progression and can be used as outcome measures for future clinical \ntrials. \n- To further understand and characterize the mechanisms of neurodegeneration in GSL and \nGP storage disorders across the spectrum of disease beginning with ganglioside storage in \nfetal life. \n \nStudy Population \nPatients with enzyme- or DNA-confirmed GM1 or GM2 gangliosidosis, sialidosis or \ngalactosialidosis. Accrual ceiling is 200 participants, with no exclusions based on age, gender, \ndemographic group, or demographic location. Patients included in our study are those who were \nseen at the NIH Clinical Center or who only sent in blood samples or who complete the \nquestionnaire or provided head circumference measures. \n \nInclusion Criteria \n- Individuals greater than 6 months of age with GM1 or GM2 gangliosidosis documented by \nenzyme deficiency and/or mutation analysis in a CLIA-approved laboratory \n \nExclusion Criteria \n \n- Individuals who in the opinion of the principal investigator are too medically fragile to   \ntravel safely to the NIH for evaluation \n- Individuals unable to comply with the protocol \n \n \n \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 19 \nNHGRI Natural History Study Age Matched Cohort \nThe data included in this investigation represents a subset of the natural history study patients. \nThis study includes only patients who had a confirmed GM1 Gangliosidosis diagnosis, excluding \nthose with other glycosphingolipid storage disorders, glycoprotein disorders, and GM2 \nGangliosidosis who were a part of the larger natural history cohort (Table A1). Patients were \nselected for the longitudinal analysis cohort based on having multiple diffusion weighted \nimaging scans with corresponding cognitive global impression (CGI) scores (Table A2). Patients \nwere selected for the age-matched cohort based on their baseline scan age and follow-up scan \n(table). Only patients who had repeated diffusion weighted imaging scans within the range of the \nnormal controls (2.5 years old – 16 years old) were included (Table A3). \n \n \nTable A1. Natural History Study Age Matched Cohort (n = 10), specific ages redacted per \nMedArXiv requirements \nParticipant Baseline Age \n(years old) \nOldest Follow-up \n(years old) \nDWI Interval \n(years) \nGM1 Subtype \nNHS 10 11-15 11-15 2.2 Juvenile \nNHS 20 11-15 11-15 3.5 Juvenile \nNHS 54 0-5 0-5 1 Juvenile \nNHS 58 6-10 11-15 4 Juvenile \nNHS 69 0-5 6-10 1.1 Juvenile \nNHS 72 6-10 6-10 0.95 Late-Infantile \nNHS 73 6-10 6-10 1.2 Late-infantile \nNHS 84 0-5 6-10 1.9 Late-infantile \nNHS 93 6-10 6-10 1 Juvenile \nNHS 94 6-10 6-10 1 Juvenile \nMean ± SD 8.45 ± 3.20 10.24 ± 3.91 1.79 ± 1.12 N/A \n \n \nTable A2. Natural History Study Longitudinal Analysis Cohort (n = 16), specific ages \nredacted per MedArXiv requirements \nParticipant GM1 \nSub-type \nBaseline Age \n(years old) \nScan #2 Age \n(years old) \nScan #3 Age \n(years old) \nAverage \nDWI \nInterval \n(years) \nNumber \nof DWI \nScans \nNHS 03 Juv 21-25 21-25 N/A 1.0 2 \nNHS 09 Juv 11-15 16-20 21-25 3.1 3 \nNHS 10 Juv 11-15 11-15 N/A 2.2 2 \nNHS 11 Juv 11-15 16-20 16-20 2.05 3 \nNHS 20 Juv 11-15 11-15 11-15 1.75 3 \nNHS 25 Juv 11-15 16-20 16-20 2.25 3 \nNHS 26 Juv 11-15 16-20 N/A 4.6 2 \nNHS 28 Juv 16-20 21-25 N/A 2.3 2 \nNHS 54 Juv 0-5 0-5 N/A 1.0 2 \nNHS 58 Juv 5-10 11-15 11-15 2.0 3 \nNHS 69 Juv 0-5 6-10 N/A 1.1 2 \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 20 \nNHS 72 LI 6-10 6-10 N/A 0.95 2 \nNHS 73 LI 6-10 6-10 N/A 1.2 2 \nNHS 84 LI 0-5 6-10 N/A 1.9 2 \nNHS 93 Juv 6-10 6-10 N/A 1.0 2 \nNHS 94 Juv 6-10 6-10 N/A 1.0 2 \n \n \nTable A3. Normal Control Age Matched Cohort (n = 32), specific ages redacted per \nMedArXiv requirements \nParticipant Baseline Age \n(years old) \nOldest Follow-up \n(years old) \nDWI Interval \n(years) \nDatabase \n10073 0-5 0-5 1.3528 Calgary \n10007 0-5 0-5 1.3528 Calgary \n10066 0-5 0-5 1.39 Calgary \n10054 0-5 6-10 2.4163 Calgary \n10148 0-5 6-10 2.1889 Calgary \n10109 0-5 6-10 2.1306 Calgary \n10022 0-5 6-10 2.37334 Calgary \n10025 0-5 6-10 2.05 Calgary \n10027 0-5 6-10 1.8889 Calgary \n10090 0-5 6-10 2.1278 Calgary \n10020 6-10 6-10 1.075 Calgary \n10087 6-10 6-10 0.8889 Calgary \n10161 6-10 6-10 1.0166 Calgary \n360 6-10 11-15 2 QTAB \n410 11-15 11-15 2 QTAB \n411 6-10 11-15 2 QTAB \n376 6-10 6-10 1 QTAB \n378 6-10 11-15 2 QTAB \n405 6-10 6-10 2 QTAB \n200 11-15 11-15 2 QTAB \n197 11-15 11-15 2 QTAB \n190 11-15 11-15 1 QTAB \n186 11-15 11-15 3 QTAB \n183 11-15 11-15 3 QTAB \n172 11-15 11-15 2 QTAB \n173 11-15 11-15 2 QTAB \n162 11-15 11-15 2 QTAB \n158 11-15 11-15 1 QTAB \n152 11-15 11-15 2 QTAB \n157 11-15 11-15 2 QTAB \n156 11-15 11-15 2 QTAB \n155 11-15 11-15 2 QTAB \nMean ± SD 8.54 ± 3.16 10.39 ± 3.32 1.85 ± 0.54 N/A \n \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 21 \nSupplement B: Diffusion Weighted Imaging (DWI) Sequence Parameters and \nProcessing \n \nNatural History Study (NHS) Patients1 \nA Philips Achieva 3T system equipped with an 8-channel SENSE head coil was used to scan all \nNatural History Study patients. DTI images were acquired with the following parameters for \nNHS: TR/TE=6400/100 ms, 32-gradient directions, b-values=0 and 1000 s/mm2, slice \nthickness=2.5 mm, acquisition matrix=128×128, NEX=1, FOV=24 cm. \n \nCalgary Normal Controls (NC)2 \nA General Electric 3T MR750w system and a 32-channel head coil was used for scanning all \nCalgary normal controls using a single shot spin echo-planar imaging sequence. DTI images \nwere acquired with the following parameters for Calgary normal controls: TR/TE=6750/79 ms, \nFOV=20 cm, 30 gradient encoding directions at b=0 and 750 s/mm2. \n \nQueensland Normal Controls(NC)3  \nA 3T Magnetom Prisma (Siemens Medical Solutions, Erlangen) and a 64-channel head coil at the \nCentre for Advanced Imaging, University of Queensland using a multi-shell with an anterior-\nposterior phase encoding direction. DTI images were acquired with the following parameters for \nQueensland normal controls: TR/TE= 3800/70 ms, voxel size=2mm x 2mm x 2mm,23-gradient \ndirections, b-values=0, 1,000, and 3,000 s/mm2, slice thickness=2 mm, FOV=244x244mm. \n \nDWI Preprocessing (Fig. B1) \nDWI was first converted from DICOM to a NIFTI file using dcm2niix where the b-values and b-\nvectors files were acquired4. DWI at all timestamps was preprocessed for artifacts, eddy currents, \nmotion, and susceptibility induced distortions using MRtrix3’s (MRtrix, v3.0.4)5 dwifslpreproc6-8 \ncommand utilizing the dwi2mask9 function followed by FSL’s (FSL, v6.0.5) eddy7 and topup7,8 \nfunctions. Preprocessed data was imported into DSI Studio (DSI Studio, v2023) where imaging \nwas quality checked for bad slices, a U-Net mask was created, and generalized q-sampling \nimaging (GQI) reconstruction was performed with a diffusion sampling length ratio of 1.2510. \n \nDWI Processing (Fig. B2) \nFirst, the fractional anisotropy (FA) map of the baseline image was exported as a NIFTI file.  \nWhole brain fiber tractography was then performed on the baseline image with 1,000,000 seeds, \na step size of 1 mm, an angular threshold of 60, minimum tract size of 20 mm, and a maximum \ntract size of 200 mm. Differential tractography was then performed on each subsequent follow-\nup scan in comparison with the baseline image where fiber tract gains and losses were calculated \nusing 10%, 20%, 30%, 40%, and 50% fractional anisotropy thresholds. Fiber tract gains were \ndetermined where the difference in FA between the follow-up and the baseline image exceeded \nthe threshold\t\n!\"#$!%!\"#$\"\n!\"#$!\n. Fiber tract losses were determined where the difference in FA between \nthe baseline and the follow-up image exceeded the threshold utilizing the equation \n!\"#$\"%!\"#$!\n!\"#$\"\n. \nDifferential tractography was calculated with the following parameters: angular threshold=60, \nstep size=1 mm, tracts < 20 mm or > 200 mm were discarded, and 1,000,000 seeds were placed. \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 22 \n \nFigure B1. DWI preprocessing pipeline. \n \n \nFigure B2. Differential Tractography overview. \n \n \n \n \n \n \n \n \n \n \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 23 \nSupplementary Results \n \nSupplement C: FA Thresholds on CGI-C \n \n \nFigure C1. Differential Tractography correlations of net fiber tract number with CGI-C \nchange scores with GM1 patients at a 10% fractional anisotropy threshold. \n \n \n \nFigure C2. Differential Tractography correlations of net fiber tract number with CGI-C \nchange scores with GM1 patients at a 20% fractional anisotropy threshold. \n \n \nFigure C3. Differential Tractography correlations of net fiber tract number with CGI-C \nchange scores with GM1 patients at a 30% fractional anisotropy threshold. \n-40 -30 -20 -10 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Number (%)\nCGI-C\nCGI-C: 10%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.5368\np < 0.0001\n-25 -20 -15 -10 -5 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Number (%)\nCGI-C\nCGI-C: FA = 20%\nMuch \nimproved\nMuch \nworse\nMinimally\nimproved\nVery much\nworse\nMinimally \nworse\nNo \nChange\nR2 = 0.3837\np < 0.0001Very much\nimproved\n-20 -15 -10 -5 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Number (%)\nCGI-C\nCGI-C: 30%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.4172\np < 0.0001\nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 24 \n \n \nFigure C4. Differential Tractography correlations of net fiber tract number with CGI-C \nchange scores with GM1 patients at a 40% fractional anisotropy threshold. \n \n \nFigure C5. Differential Tractography correlations of net fiber tract number with CGI-C \nchange scores with GM1 patients at a 50% fractional anisotropy threshold. \n \nFigure C6. Differential Tractography correlations of net fiber tract volume with CGI-C \nchange scores with GM1 patients at a 10% fractional anisotropy threshold. \n \n-15 -10 -5 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Number (%)\nCGI-C\nCGI-C: 40%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.3304\np < 0.0001\n-15 -10 -5 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Number (%)\nCGI-C\nCGI-C: 50%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.2410\np = 0.0012\n-50 -40 -30 -20 -10 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Volume (%)\nCGI-C\nCGI-C:Volume - 10%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.5995\np < 0.0001\nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 25 \n \nFigure C7. Differential Tractography correlations of net fiber tract volume with CGI-C \nchange scores with GM1 patients at a 20% fractional anisotropy threshold. \n \n \nFigure C8. Differential Tractography correlations of net fiber tract volume with CGI-C \nchange scores with GM1 patients at a 30% fractional anisotropy threshold. \n \nFigure C9. Differential Tractography correlations of net fiber tract volume with CGI-C \nchange scores with GM1 patients at a 40% fractional anisotropy threshold. \n \n-40 -30 -20 -10 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Volume (%)\nCGI-C\nCGI-C:Volume - 20%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.4833\np < 0.0001\n-25 -20 -15 -10 -5 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Volume (%)\nCGI-C\nCGI-C:Volume - 30%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.5173\np < 0.0001\n-15 -10 -5 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Volume (%)\nCGI-C\nCGI-C:Volume - 40%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.4395\np < 0.0001\nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 26 \n \nFigure C10. Differential Tractography correlations of net fiber tract volume with CGI-C \nchange scores with GM1 patients at a 50% fractional anisotropy threshold. \n \nTable C1. Correlations between net fiber tract number and net fiber tract \nvolume with longitudinal CGI-C scores at varying fractional anisotropy \nthresholds. \nMetric FA Threshold 𝜒2 R2 p-value \nNet Fiber Tract \nNumber \n10% 29.04 0.5368 p < 0.0001 \nNet Fiber Tract \nNumber \n20% 18.31 0.3837 p < 0.0001 \nNet Fiber Tract \nNumber \n30% 20.41 0.4172 p < 0.0001 \nNet Fiber Tract \nNumber \n40% 15.19 0.3304 p < 0.0001 \nNet Fiber Tract \nNumber \n50% 10.46 0.241 p = 0.0012 \nNet Fiber Tract \nV olume \n10% 34.48 0.5995 p < 0.0001 \nNet Fiber Tract \nV olume \n20% 36.58 0.4176 p < 0.0001 \nNet Fiber Tract \nV olume \n30% 27.49 0.5173 p < 0.0001 \nNet Fiber Tract \nV olume \n40% 21.88 0.4395 p < 0.0001 \nNet Fiber Tract \nV olume \n50% 16.90 0.3601 p < 0.0001 \n \n \n \n \n \n \n-15 -10 -5 0\n1\n2\n3\n4\n5\n6\n7\nNet Fiber Tract Volume (%)\nCGI-C\nCGI-C:Volume - 40%\nMinimally \nworse\nMuch \nworse\nMinimally\nimproved\nNo \nChange\nVery much\nimproved\nVery much\nworse\nMuch \nimproved\nR2 = 0.3601\np < 0.0001\nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint \n\nGM1 Differential Tractography \n 27 \nSupplementary References \n1. National Human Genome Research Institute. Natural History of Glycosphingolipid Storage \nDisorders and Glycoprotein Disorders ClinicalTrials.gov identifier: NCT00029965. Updated \nAugust 7, 2024. Accessed August 12, 2024. https://clinicaltrials.gov/study/NCT00029965  \n2. Reynolds JE, Long X, Paniukov D, Bagshawe M, Lebel C. Calgary Preschool magnetic \nresonance imaging (MRI) dataset. Data Brief. 2020 Jan 31;29:105224. doi: \n10.1016/j.dib.2020.105224. PMID: 32071993; PMCID: PMC7016255 \n3. Strike, L.T., Hansell, N.K., Chuang, KH. et al. The Queensland Twin Adolescent Brain Project, \na longitudinal study of adolescent brain development. Sci Data 10, 195 (2023). \nhttps://doi.org/10.1038/s41597-023-02038-w \n4. Li X, Morgan PS, Ashburner J, Smith J, Rorden C. The first step for neuroimaging data \nanalysis: DICOM to NIfTI conversion. J Neurosci Methods. 2016 May 1;264:47 -56. doi: \n10.1016/j.jneumeth.2016.03.001. Epub 2016 Mar 2. PMID: 26945974. \n5. Tournier, J. D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., \nJeurissen, B., Yeh, C. H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software \nframework for medical image processing and visualisation. NeuroImage, 202, 116137. \nhttps://doi.org/10.1016/j.neuroimage.2019.116137 \n6. Andersson, J. L. R., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-\nresonance effects and subject movement in diffusion MR imaging. NeuroImage, 125, 1063 –\n1078. https://doi.org/10.1016/j.neuroimage.2015.10.019 \n7. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen -\nBerg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., \nVickers, J., Zhang, Y ., De Stefano, N., Brady, J. M., & Matthews, P. M. (2004). Advances in \nfunctional and structural MR image analysis and implementation as FSL. NeuroImage, 23 \nSuppl 1, S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051 \n8. Andersson, J. L., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions \nin spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 20(2), \n870–888. https://doi.org/10.1016/S1053-8119(03)00336-7 \n9. Cox R. W. (1996). AFNI: software for analysis and visualization of functional magnetic \nresonance neuroimages. Computers and biomedical research, an international journal, 29(3), \n162–173. https://doi.org/10.1006/cbmr.1996.0014 \n10. Yeh, F. C., Wedeen, V . J., & Tseng, W. Y . (2010). Generalized q -sampling imaging. IEEE \ntransactions on medical imaging, 29(9), 1626 –1635. \nhttps://doi.org/10.1109/TMI.2010.2045126. \nfor use under a CC0 license. \nThis article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted August 26, 2024. ; https://doi.org/10.1101/2024.08.25.24312255doi: medRxiv preprint","source_license":"Public-Domain","license_restricted":false}