{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:09:14Z","timestamp":1722384554071},"reference-count":12,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economy and Competitiveness","award":["MTM2017-83513-R"]},{"name":"Competitive Research Unit Consolidation Programme of the Galician Regional Authority","award":["ED431C-2020-20"]},{"name":"UE","award":["EAPA-791\/2018","0624-2iqbioneuro-6"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"In the field of medical imaging, one of the most extended research setups consists of the comparison between two groups of images, a pathological set against a control set, in order to search for statistically significant differences in brain activity. Functional Data Analysis (FDA), a relatively new field of statistics dealing with data expressed in the form of functions, uses methodologies which can be easily extended to the study of imaging data. Examples of this have been proposed in previous publications where the authors settle the mathematical groundwork and properties of the proposed estimators. The methodology herein tested allows for the estimation of mean functions and simultaneous confidence corridors (SCC), also known as simultaneous confidence bands, for imaging data and for the difference between two groups of images. FDA applied to medical imaging presents at least two advantages compared to previous methodologies: it avoids loss of information in complex data structures and avoids the multiple comparison problem arising from traditional pixel-to-pixel comparisons. Nonetheless, computing times for this technique have only been explored in reduced and simulated setups. In the present article, we apply this procedure to a practical case with data extracted from open neuroimaging databases; then, we measure computing times for the construction of Delaunay triangulations and for the computation of mean function and SCC for one-group and two-group approaches. The results suggest that the previous researcher has been too conservative in parameter selection and that computing times for this methodology are reasonable, confirming that this method should be further studied and applied to the field of medical imaging.<\/jats:p>","DOI":"10.3390\/computers11060091","type":"journal-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T15:05:02Z","timestamp":1654182302000},"page":"91","source":"Crossref","is-referenced-by-count":0,"title":["Functional Data Analysis for Imaging Mean Function Estimation: Computing Times and Parameter Selection"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3355-6393","authenticated-orcid":false,"given":"Juan A.","family":"Arias-L\u00f3pez","sequence":"first","affiliation":[{"name":"Biostatistics and Biomedical Data Science Unit, Department of Statistics, Mathematical Analysis, and Operational Research, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain"},{"name":"CITMAga, 15782 Santiago de Compostela, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9893-9520","authenticated-orcid":false,"given":"Carmen","family":"Cadarso-Su\u00e1rez","sequence":"additional","affiliation":[{"name":"Biostatistics and Biomedical Data Science Unit, Department of Statistics, Mathematical Analysis, and Operational Research, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain"},{"name":"CITMAga, 15782 Santiago de Compostela, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7322-2195","authenticated-orcid":false,"given":"Pablo","family":"Aguiar-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Nuclear Medicine Department and Molecular Imaging Group, University Clinical Hospital (CHUS) and Health Research Institute of Santiago de Compostela (IDIS), 15705 Santiago de Compostela, Spain"},{"name":"Molecular Imaging Group, Department of Psychiatry, Radiology and Public Health, Faculty of Medicine, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ramsay, J.O. (2004). Functional data analysis. Encyclopedia of Statistical Sciences, John Wiley & Sons.","DOI":"10.1002\/0471667196.ess0646"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1146\/annurev-statistics-041715-033624","article-title":"Functional data analysis","volume":"3","author":"Wang","year":"2016","journal-title":"Annu. Rev. Stat. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S189","DOI":"10.1016\/j.neuroimage.2004.07.026","article-title":"Unified univariate and multivariate random field theory","volume":"23","author":"Worsley","year":"2004","journal-title":"NeuroImage"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1111\/biom.13156","article-title":"Simultaneous confidence corridors for mean functions in functional data analysis of imaging data","volume":"76","author":"Wang","year":"2020","journal-title":"Biometrics"},{"key":"ref_5","unstructured":"Lai, M.J., and Wang, L. (Bivariate Spline over Triangulation, 2019). Bivariate Spline over Triangulation, R Package Version 0.1.0."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.5705\/ss.2009.207","article-title":"Simultaneous confidence bands for nonparametric regression with functional data","volume":"21","author":"Degras","year":"2011","journal-title":"Stat. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gervasi, O., Murgante, B., Misra, S., Garau, C., Ble\u010di\u0107, I., Taniar, D., Apduhan, B.O., Rocha, A.M.A.C., Tarantino, E., and Torre, C.M. (2021). Computational Issues in the Application of Functional Data Analysis to Imaging Data. 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Statistical Parametric Mapping: The Analysis of Functional Brain Images, Elsevier."},{"key":"ref_11","unstructured":"Lai, M.J., and Wang, L. (Triangulation: Triangulation in 2D Domain, 2020). Triangulation: Triangulation in 2D Domain, R Package Version 0.1.0."},{"key":"ref_12","unstructured":"Wang, Y., Wang, G., and Wang, L. (ImageSCC: SCC for Mean Function of Imaging Data, 2020). ImageSCC: SCC for Mean Function of Imaging Data, R Package Version 0.1.0."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/6\/91\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T20:06:09Z","timestamp":1722369969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/6\/91"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,2]]},"references-count":12,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["computers11060091"],"URL":"https:\/\/doi.org\/10.3390\/computers11060091","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202204.0068.v1","asserted-by":"object"}]},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2022,6,2]]}}}