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dc.contributor.authorBadrulhisham, F
dc.contributor.authorPogatzki-Zahn, E
dc.contributor.authorSegelcke, D
dc.contributor.authorSpisak, T
dc.contributor.authorVollert, J
dc.date.accessioned2023-12-18T14:28:41Z
dc.date.issued2023-11-14
dc.date.updated2023-12-18T13:25:35Z
dc.description.abstractArtificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.en_GB
dc.format.extent470-479
dc.format.mediumPrint-Electronic
dc.identifier.citationVol. 115, pp. 470-479en_GB
dc.identifier.doihttps://doi.org/10.1016/j.bbi.2023.11.005
dc.identifier.urihttp://hdl.handle.net/10871/134818
dc.identifierORCID: 0000-0003-0733-5201 (Vollert, Jan)
dc.identifierScopusID: 55985922500 (Vollert, Jan)
dc.identifierResearcherID: AAJ-7461-2020 (Vollert, Jan)
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pubmed/37972877en_GB
dc.rights© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/)en_GB
dc.subject*omicsen_GB
dc.subjectArtificial intelligenceen_GB
dc.subjectBehavioural researchen_GB
dc.subjectMachine learningen_GB
dc.subjectNeuroscienceen_GB
dc.subjectPainen_GB
dc.subjectPredictive modellingen_GB
dc.subjectfMRIen_GB
dc.titleMachine learning and artificial intelligence in neuroscience: A primer for researchersen_GB
dc.typeArticleen_GB
dc.date.available2023-12-18T14:28:41Z
dc.identifier.issn0889-1591
exeter.place-of-publicationNetherlands
dc.descriptionThis is the final version. Available on open access from Elsevier via the DOI in this recorden_GB
dc.descriptionData availability: No data was used for the research described in the article.en_GB
dc.identifier.eissn1090-2139
dc.identifier.journalBrain, Behavior, and Immunityen_GB
dc.relation.ispartofBrain Behav Immun, 115
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_GB
dcterms.dateAccepted2023-11-08
dc.rights.licenseCC BY-NC
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2023-11-14
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2023-12-18T14:27:02Z
refterms.versionFCDVoR
refterms.dateFOA2023-12-18T14:28:51Z
refterms.panelAen_GB
refterms.dateFirstOnline2023-11-14


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© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/)
Except where otherwise noted, this item's licence is described as © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/)