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Identification of individual subjects on the basis of their brain anatomical features.

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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Valizadeh SA,Liem F,Mérillat S,Hänggi J,Jäncke L

doi

10.1038/s41598-018-23696-6

subject

Has Abstract

pub_date

2018-04-04 00:00:00

pages

5611

issue

1

issn

2045-2322

pii

10.1038/s41598-018-23696-6

journal_volume

8

pub_type

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