{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T12:52:03Z","timestamp":1782823923939,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background: Spinocerebellar ataxia type 2 (SCA2) is an inherited neurodegenerative disorder characterized by progressive cerebellar degeneration. One difficulty in treating this disease lies in identifying preclinical carriers: individuals who carry the pathogenic ATXN2 mutation but remain asymptomatic with respect to motor manifestations. Though magnetic resonance imaging (MRI) has proven valuable in supporting the diagnosis of ataxia, traditional univariate approaches using linear measurements have shown limited ability to capture the complex anatomical changes that occur across the disease spectrum, particularly during the preclinical phase. Methods: This study employed a comprehensive multivariate approach to improve the classification of individuals across the SCA2 spectrum. We developed a multinomial logistic regression model incorporating multiple linear measurements derived from magnetic resonance imaging to discriminate between healthy controls (n = 72), preclinical carriers (n = 17), and patients with manifest SCA2 (n = 61). To mitigate inherent class imbalance, particularly in the smaller preclinical subgroup, we implemented the Synthetic Minority Over-sampling Technique (SMOTE), generating a balanced dataset that enhances the model\u2019s ability to discern the distinctive anatomical features. This was compared to the model applied to the unbalanced data. An improvement was observed when applying SMOTE. Results: The multivariate model demonstrated discriminatory performance, achieving an overall accuracy of 80.7%. The ability to identify healthy controls (AUC: 0.96), preclinical individuals (AUC: 0.75), and clinical individuals (AUC: 95%). This represents an advance over previous univariate approaches, which have had difficulty capturing the neurodegenerative changes characteristic of the preclinical stage. Conclusions: By integrating multiple neuroimaging biomarkers into a multivariable model, this study provides a tool for early identification of preclinical SCA2 carriers. The ability to accurately classify these individuals opens an opportunity for early therapeutic intervention before irreversible neurological deterioration occurs. This approach shows promise for optimizing clinical trial design and personalized care in SCA2.<\/jats:p>","DOI":"10.3390\/tomography12070092","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T10:01:17Z","timestamp":1782813677000},"page":"92","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the Identification of the Preclinical Stages of Spinocerebellar Ataxia Type 2"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0619-6844","authenticated-orcid":false,"given":"Camilo","family":"Mora-Batista","sequence":"first","affiliation":[{"name":"Facultad de Matem\u00e1ticas, Universidad Aut\u00f3noma de Guerrero, Chilpancingo de los Bravo 39087, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9428-3619","authenticated-orcid":false,"given":"Cruz","family":"Vargas-De-Le\u00f3n","sequence":"additional","affiliation":[{"name":"Divisi\u00f3n de Investigaci\u00f3n, Hospital Ju\u00e1rez de M\u00e9xico, Ciudad de M\u00e9xico 07760, Mexico"},{"name":"Laboratorio de Modelaci\u00f3n Bioestad\u00edstica para la Salud, Secci\u00f3n de Estudios de Posgrado e Investigaci\u00f3n, Escuela Superior de Medicina, Instituto Polit\u00e9cnico Nacional, Ciudad de M\u00e9xico 11340, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram\u00f3n","family":"Reyes-Carreto","sequence":"additional","affiliation":[{"name":"Facultad de Matem\u00e1ticas, Universidad Aut\u00f3noma de Guerrero, Chilpancingo de los Bravo 39087, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank J.","family":"Carrillo-Rodes","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n y Rehabilitaci\u00f3n de las Ataxias Hereditarias, VPWP+RM5, Holgu\u00edn 80100, Cuba"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Alberto","family":"\u00c1lvarez-Cuesta","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n y Rehabilitaci\u00f3n de las Ataxias Hereditarias, VPWP+RM5, Holgu\u00edn 80100, Cuba"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41572-019-0074-3","article-title":"Spinocerebellar ataxia","volume":"5","author":"Klockgether","year":"2019","journal-title":"Nat. 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