A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in alzheimer's disease

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• dc.contributor.author Martí Juan, Gerard
• dc.contributor.author Sanromà, Gerard
• dc.contributor.author Piella Fenoy, Gemma
• dc.date.accessioned 2020-10-14T08:18:44Z
• dc.date.issued 2020
• dc.description.abstract Background and Objectives: Recently, longitudinal studies of Alzheimer’s disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer’s disease using longitudinal neuroimaging. Methods: We search for papers using longitudinal imaging data, focused on Alzheimer’s Disease and published between 2007 and 2019 on four different search engines. Results: After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions. Conclusions: Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.en
• dc.description.sponsorship This research was partially funded by the “Fundació La Marató de TV3” (no20154031). This work was also funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme [MDM-2015-0502]. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence755 the work reported in this paper.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Martí-Juan G, Sanroma-Guell G, Piella G. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in alzheimer's disease. Comput Methods Programs Biomed. 2020 Jan 20;189:105348. DOI: 10.1016/j.cmpb.2020.105348
• dc.identifier.doi http://dx.doi.org/10.1016/j.cmpb.2020.105348
• dc.identifier.issn 0169-2607
• dc.identifier.uri http://hdl.handle.net/10230/45480
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Computer Methods and Programs in Biomedicine. 2020 Jan 20;189:105348
• dc.rights © Elsevier http://dx.doi.org/10.1016/j.cmpb.2020.105348
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Longitudinalen
• dc.subject.keyword Disease progressionen
• dc.subject.keyword Alzheimer’s diseaseen
• dc.subject.keyword Machine learningen
• dc.title A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in alzheimer's diseaseen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion