Abstract
We analysed a dataset comprising 118 subjects who were scanned three times (at baseline, 1-year follow-up, and 7-year follow-up) using structural magnetic resonance imaging (MRI) over the course of 7 years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years 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-based 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-then rules and compared to the same set of neuroanatomical features derived from the 7-year follow-up 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-feature 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-fingerprinting techniques.