{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:38:20Z","timestamp":1768567100272,"version":"3.49.0"},"posted":{"date-parts":[[2025,2,20]]},"group-title":"Genetic and Genomic Medicine","reference-count":66,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,2,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    We found volBrain\u2019s\n                    <jats:italic>vol2Brain<\/jats:italic>\n                    pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer\u2019s\n                    <jats:italic>recon-all<\/jats:italic>\n                    pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain\u2019s\n                    <jats:italic>vol2Brain<\/jats:italic>\n                    and SimNIBS\u2019\n                    <jats:italic>headreco<\/jats:italic>\n                    to have the strongest correlations depending on the cohort. For the lentiform nucleus, we found a combination of\n                    <jats:italic>recon-all<\/jats:italic>\n                    and FSL\u2019s\n                    <jats:italic>FIRST<\/jats:italic>\n                    to give the strongest correlations depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.02.18.25322304","type":"posted-content","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T19:05:16Z","timestamp":1740078316000},"source":"Crossref","is-referenced-by-count":0,"title":["A Case for Automated Segmentation of MRI Data in Milder Neurodegenerative Diseases"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8204-5848","authenticated-orcid":false,"given":"Connor J.","family":"Lewis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean M.","family":"Johnston","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Precilla","family":"D\u2019Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josephine","family":"Kolstad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Zoppo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeynep","family":"Vardar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna Luisa","family":"K\u00fchn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet","family":"Peker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zubir S.","family":"Rentiya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William A.","family":"Gahl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed Salman","family":"Shazeeb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cynthia J.","family":"Tifft","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8914-7401","authenticated-orcid":false,"given":"Maria T.","family":"Acosta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025022202350672000_2025.02.18.25322304v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pscychresns.2011.01.007"},{"key":"2025022202350672000_2025.02.18.25322304v1.2","doi-asserted-by":"publisher","DOI":"10.1017\/S1041610202008281"},{"key":"2025022202350672000_2025.02.18.25322304v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116450"},{"key":"2025022202350672000_2025.02.18.25322304v1.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.12.033"},{"key":"2025022202350672000_2025.02.18.25322304v1.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120292"},{"key":"2025022202350672000_2025.02.18.25322304v1.6","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.07.006"},{"key":"2025022202350672000_2025.02.18.25322304v1.7","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23671"},{"key":"2025022202350672000_2025.02.18.25322304v1.8","doi-asserted-by":"publisher","DOI":"10.3389\/fnimg.2024.1410848"},{"key":"2025022202350672000_2025.02.18.25322304v1.9","doi-asserted-by":"publisher","DOI":"10.1177\/0972753121990175"},{"key":"2025022202350672000_2025.02.18.25322304v1.10","doi-asserted-by":"publisher","DOI":"10.4103\/0971-6203.58777"},{"key":"2025022202350672000_2025.02.18.25322304v1.11","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-09749-7_12"},{"key":"2025022202350672000_2025.02.18.25322304v1.12","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10121435"},{"key":"2025022202350672000_2025.02.18.25322304v1.13","doi-asserted-by":"publisher","DOI":"10.1186\/s40478-023-01509-w"},{"key":"2025022202350672000_2025.02.18.25322304v1.14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-60789-x"},{"key":"2025022202350672000_2025.02.18.25322304v1.15","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-52966-0"},{"key":"2025022202350672000_2025.02.18.25322304v1.16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-38833-z"},{"key":"2025022202350672000_2025.02.18.25322304v1.17","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-09170-y"},{"key":"2025022202350672000_2025.02.18.25322304v1.18","doi-asserted-by":"publisher","DOI":"10.3390\/s20113243"},{"key":"2025022202350672000_2025.02.18.25322304v1.19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.021"},{"key":"2025022202350672000_2025.02.18.25322304v1.20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-54663-z"},{"key":"2025022202350672000_2025.02.18.25322304v1.21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117287"},{"key":"2025022202350672000_2025.02.18.25322304v1.22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0085618"},{"key":"2025022202350672000_2025.02.18.25322304v1.23","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2024.1338743"},{"key":"2025022202350672000_2025.02.18.25322304v1.24","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.10.3.034501"},{"key":"2025022202350672000_2025.02.18.25322304v1.25","doi-asserted-by":"publisher","DOI":"10.1016\/j.gim.2024.101144"},{"key":"2025022202350672000_2025.02.18.25322304v1.26","doi-asserted-by":"publisher","DOI":"10.1002\/jimd.12646"},{"key":"2025022202350672000_2025.02.18.25322304v1.27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymgme.2019.12.012"},{"key":"2025022202350672000_2025.02.18.25322304v1.28","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.734878"},{"issue":"4","key":"2025022202350672000_2025.02.18.25322304v1.29","doi-asserted-by":"crossref","first-page":"352","DOI":"10.2174\/1566523222666220304092732","article-title":"AAV9-coGLB1 Improves Lysosomal Storage and Rescues Central Nervous System Inflammation in a Mutant Mouse Model of GM1 Gangliosidosis","volume":"22","year":"2022","journal-title":"Current gene therapy"},{"key":"2025022202350672000_2025.02.18.25322304v1.30","doi-asserted-by":"publisher","DOI":"10.1016\/j.omtm.2019.11.023"},{"key":"2025022202350672000_2025.02.18.25322304v1.31","doi-asserted-by":"publisher","DOI":"10.1002\/ajmg.a.37468"},{"key":"2025022202350672000_2025.02.18.25322304v1.32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymgme.2017.12.432"},{"key":"2025022202350672000_2025.02.18.25322304v1.33","unstructured":"National Human Genome Research Institute. Natural History of Glycosphingolipid Storage Disorders and Glycoprotein Disorders ClinicalTrials.gov identifier: NCT00029965. Updated August 7, 2024. Accessed August 12, 2024. https:\/\/clinicaltrials.gov\/study\/NCT00029965."},{"key":"2025022202350672000_2025.02.18.25322304v1.34","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105224"},{"key":"2025022202350672000_2025.02.18.25322304v1.35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0182340"},{"key":"2025022202350672000_2025.02.18.25322304v1.36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2016.03.001"},{"key":"2025022202350672000_2025.02.18.25322304v1.37","doi-asserted-by":"publisher","DOI":"10.1097\/00004728-199403000-00005"},{"key":"2025022202350672000_2025.02.18.25322304v1.38","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.1998.0395"},{"key":"2025022202350672000_2025.02.18.25322304v1.39","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.1998.0396"},{"key":"2025022202350672000_2025.02.18.25322304v1.40","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4"},{"key":"2025022202350672000_2025.02.18.25322304v1.41","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.200033797"},{"key":"2025022202350672000_2025.02.18.25322304v1.42","doi-asserted-by":"publisher","DOI":"10.1109\/42.906426"},{"key":"2025022202350672000_2025.02.18.25322304v1.43","unstructured":"\u201cNon-uniform intensity correction,\u201d http:\/\/www.bic.mni.mcgill.ca\/software\/N3\/node6.html."},{"key":"2025022202350672000_2025.02.18.25322304v1.44","doi-asserted-by":"publisher","DOI":"10.1016\/S0896-6273(02)00569-X"},{"key":"2025022202350672000_2025.02.18.25322304v1.45","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhg087"},{"key":"2025022202350672000_2025.02.18.25322304v1.46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.10.055"},{"key":"2025022202350672000_2025.02.18.25322304v1.47","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2004.07.051"},{"key":"2025022202350672000_2025.02.18.25322304v1.48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.015"},{"key":"2025022202350672000_2025.02.18.25322304v1.49","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2016.00030"},{"key":"2025022202350672000_2025.02.18.25322304v1.50","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2022.862805"},{"key":"2025022202350672000_2025.02.18.25322304v1.51","unstructured":"Penny, W. D. , Friston, K. J. , Ashburner, J. T. , Kiebel, S. J. , & Nichols, T. E . (Eds.). (2011). Statistical parametric mapping: the analysis of functional brain images. Elsevier."},{"key":"2025022202350672000_2025.02.18.25322304v1.52","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.03.001"},{"key":"2025022202350672000_2025.02.18.25322304v1.53","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.09.050"},{"key":"2025022202350672000_2025.02.18.25322304v1.54","doi-asserted-by":"publisher","DOI":"10.1162\/imag_a_00090"},{"key":"2025022202350672000_2025.02.18.25322304v1.55","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2019.108565"},{"key":"2025022202350672000_2025.02.18.25322304v1.56","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.10123"},{"key":"2025022202350672000_2025.02.18.25322304v1.57","doi-asserted-by":"publisher","DOI":"10.1111\/jon.12838"},{"key":"2025022202350672000_2025.02.18.25322304v1.58","doi-asserted-by":"publisher","DOI":"10.1111\/jon.12972"},{"key":"2025022202350672000_2025.02.18.25322304v1.59","doi-asserted-by":"publisher","DOI":"10.1117\/12.911504"},{"key":"2025022202350672000_2025.02.18.25322304v1.60","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymgme.2025.109025"},{"key":"2025022202350672000_2025.02.18.25322304v1.61","doi-asserted-by":"publisher","DOI":"10.1016\/S1076-6332(03)00671-8"},{"key":"2025022202350672000_2025.02.18.25322304v1.62","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/56\/14\/021"},{"key":"2025022202350672000_2025.02.18.25322304v1.63","doi-asserted-by":"publisher","DOI":"10.1186\/s41747-020-00200-2"},{"key":"2025022202350672000_2025.02.18.25322304v1.64","unstructured":"National Human Genome Research Institute. Natural History of Glycosphingolipid Storage Disorders and Glycoprotein Disorders ClinicalTrials.gov identifier: NCT00029965. Updated August 7, 2024. Accessed August 12, 2024. https:\/\/clinicaltrials.gov\/study\/NCT00029965."},{"key":"2025022202350672000_2025.02.18.25322304v1.65","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105224"},{"key":"2025022202350672000_2025.02.18.25322304v1.66","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0182340"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2025.02.18.25322304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:28:32Z","timestamp":1768508912000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2025.02.18.25322304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,20]]},"references-count":66,"URL":"https:\/\/doi.org\/10.1101\/2025.02.18.25322304","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.3390\/neurosci6020031","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2025,2,20]]},"subtype":"preprint"}}