Identification of individual subjects on the basis of their brain anatomical features
Seyed Abolfazl Valizadeh, Franziskus Liem, Susan Mérillat, Jürgen Hänggi & Lutz Jäncke
Scientific Reports volume 8, Article number: 5611 (2018)
Three brain scans (from the front, side and above) of two different brains (pictured on the left and on the right) belonging to twins. The furrows and ridges are different in each person.
Credit: Lutz Jaencke, UZH
Abstract
We examined whether it is possible to identify individual subjects on the basis of brain anatomical features. For this, we analyzed a dataset comprising 191 subjects who were scanned three times over a period of two years. Based on FreeSurfer routines, we generated three datasets covering 148 anatomical regions (cortical thickness, area, volume). These three datasets were also combined to a dataset containing all of these three measures. In addition, we used a dataset comprising 11 composite anatomical measures for which we used larger brain regions (11LBR). These datasets were subjected to a linear discriminant analysis (LDA) and a weighted K-nearest neighbors approach (WKNN) to identify single subjects. For this, we randomly chose a data subset (training set) with which we calculated the individual identification. The obtained results were applied to the remaining sample (test data). In general, we obtained excellent identification results (reasonably good results were obtained for 11LBR using WKNN). Using different data manipulation techniques (adding white Gaussian noise to the test data and changing sample sizes) still revealed very good identification results, particularly for the LDA technique. Interestingly, using the small 11LBR dataset also revealed very good results indicating that the human brain is highly individual.
Acknowledgements
The current analysis incorporates data from the Longitudinal Healthy Aging Brain (LHAB) database project, which is carried out as one of the core projects at the International Normal Aging and Plasticity Imaging Center/INAPIC and the University Research Priority Program “Dynamics of Healthy Aging” of the University of Zurich. This work was supported by the Velux Stiftung (project No. 369), by the University Research Priority Program “Dynamics of Healthy Aging” of the University of Zurich. We would also like to thank professor Carolin Strobl (Department of Psychology, University of Zurich) and professor Robert Riener (Department of Health Sciences and Technology, ETH Zurich) for their contribution to this paper. All subjects gave written informed consent prior to participating in the study. In addition, all methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the ethical committee of the canton of Zurich (KEK-ZH-Nr. 2010–0267).
Author information
Affiliations
Division Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
Seyed Abolfazl Valizadeh, Jürgen Hänggi & Lutz Jäncke
Sensory-Motor Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
Seyed Abolfazl Valizadeh
International Normal Aging and Plasticity Imaging Center (INAPIC), University of Zurich, Zurich, Switzerland
Susan Mérillat & Lutz Jäncke
University Research Priority Program (URPP) “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
Franziskus Liem, Susan Mérillat & Lutz Jäncke
Contributions
S.V. wrote the Matlab code, conducted the analyses, interpreted the data, prepared figures, and wrote the main manuscript; L.J. participated in data analysis, design of the study, interpretation, and writing of the manuscript; F.L. S.M., J.H. participated in interpretation and writing of the manuscript.
Competing Interests
The authors declare no competing interests.
Corresponding author
Correspondence to Lutz Jäncke.
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Publication history
Received 13 November 2017 Accepted 14 March 2018
Published 04 April 2018
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