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We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7\u2009years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume [ICV], total cortical thickness [CT], total cortical surface area [CA], cortical grey matter [CoGM], cerebral white matter [CeWM], cerebellar cortex [CBGM], cerebellar white matter [CBWM], subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala and accumbens] and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF) and a newly developed rule\u2010based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if\u2013then rules and compared to the same set of neuroanatomical features derived from the 7\u2010year follow\u2010up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some four\u2010feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain\u2010fingerprinting techniques.<\/jats:p>","DOI":"10.1111\/ejn.15770","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:40:13Z","timestamp":1657759213000},"page":"4642-4652","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification of individual subjects based on neuroanatomical measures obtained 7\u2009years earlier"],"prefix":"10.1111","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2110-9067","authenticated-orcid":false,"given":"Lutz","family":"J\u00e4ncke","sequence":"first","affiliation":[{"name":"Division Neuropsychology, Department of Psychology University of Zurich  Zurich Switzerland"},{"name":"University Research Priority Program \u201cDynamics of Healthy Aging\u201d University Zurich  Zurich Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0856-8541","authenticated-orcid":false,"given":"Seyed A.","family":"Valizadeh","sequence":"additional","affiliation":[{"name":"Division Neuropsychology, Department of Psychology University of Zurich  Zurich 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