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One field of extensive clinical use of MRI is the accurate and automated classification of degenerative disorders. Most of current classification studies either do not mirror medical practice where patients may exhibit early stages of the disease, comorbidities, or atypical variants, or they are not able to produce probabilistic predictions nor account for uncertainty. Also, the spatial heterogeneity of the brain alterations caused by neurodegenerative processes is not usually considered, despite the spatial configuration of the neuronal loss is a characteristic hallmark for each disorder. In this article, we propose a classification technique that incorporates uncertainty and spatial information for distinguishing between healthy subjects and patients from four distinct neurodegenerative diseases: Alzheimer's disease, mild cognitive impairment, Parkinson's disease, and Multiple Sclerosis. We introduce a spatially informed Bayesian neural network (SBNN) that combines a three\u2010dimensional neural network to extract neurodegeneration features from MRI, Bayesian inference to account for uncertainty in diagnosis, and a spatially informed MRI image using hidden Markov random fields to encode cerebral spatial information. The SBNN model demonstrates that classification accuracy increases up to 25% by including a spatially informed MRI scan. Furthermore, the SBNN provides a robust probabilistic diagnosis that resembles clinical decision\u2010making and can account for the heterogeneous medical presentations of neurodegenerative disorders.<\/jats:p>","DOI":"10.1002\/sim.9604","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T12:29:55Z","timestamp":1669638595000},"page":"105-121","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Spatially informed Bayesian neural network for neurodegenerative diseases classification"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6130-7450","authenticated-orcid":false,"given":"David","family":"Payares\u2010Garcia","sequence":"first","affiliation":[{"name":"ITC Faculty Geo\u2010Information Science and Earth Observation University of Twente  Enschede"},{"name":"Institute for Geoinformatics University of M\u00fcnster  M\u00fcnster Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2868-7604","authenticated-orcid":false,"given":"Jorge","family":"Mateu","sequence":"additional","affiliation":[{"name":"Department of Mathematics University Jaume I  Castell\u00f3n de la Plana Castell\u00f3n Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wiebke","family":"Schick","sequence":"additional","affiliation":[{"name":"Institute for Geoinformatics University of M\u00fcnster  M\u00fcnster Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a028035"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nj7526-299a"},{"issue":"5","key":"e_1_2_11_4_1","first-page":"1","article-title":"Neuroimaging in the early diagnosis of neurodegenerative diseases","volume":"1","author":"Stoessl AJ","year":"2012","journal-title":"Transl Neurodegener"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a006189"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.518"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awh088"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2014.11.001"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00784"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.118.219097"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuri.2021.100012"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-658-25326-4_18"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-9939-8_27"},{"key":"e_1_2_11_14_1","volume-title":"IEEE Computational Intelligence Magazine","author":"Valentin Jospin L","year":"2022"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46819-8_67"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2014.00156"},{"issue":"2","key":"e_1_2_11_17_1","first-page":"797","article-title":"Spatial modeling of multiple sclerosis for disease subtype prediction","volume":"17","author":"Taschler B","year":"2014","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2017.12.001"},{"key":"e_1_2_11_19_1","doi-asserted-by":"crossref","unstructured":"MeiS JiJ BiQ HouJ DuQ LiW.Integrating spectral and spatial information into deep convolutional neural networks for hyperspectral classification. 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