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.

Weak correlations between body height and several brain metrics in healthy elderly subjects

“…MRI data were acquired with a 3.0T Philips Ingenia scanner (Philips Medical Systems). We have described the image acquisition procedure in several of our recent papers; thus, we partly reiterate what we have mentioned in these papers (Hirsiger et al., ; Jäncke et al., ; Madhyastha et al., ; Valizadeh et al., ). T1‐weighted images were recorded with a gradient echo sequence (3D turbo field echo, 160 sagittal slices, slice thickness = 1 mm, in‐plane resolution = 1 × 1 mm, FOV = 240 × 240 mm, repetition time = 8.18 ms, echo time = 3.80 ms, flip angle = 8°).…”

“…Participation was voluntary, and all subjects gave written informed consent in accordance with the declaration of Helsinki. The data of this sample have been used in previous publications of our group (Hirsiger et al., ; Jäncke, Mérillat, Liem, & Hänggi, ; Madhyastha et al., ; Valizadeh, Hänggi, Mérillat, & Jäncke, ; Valizadeh, Liem, Mérillat, Hänggi, & Jäncke, ).…”

Scaling of brain compartments to brain size

“…In this paper, we will not report the results with respect to the longitudinal data set; we will rather focus on the first wave of data acquisition. Several papers have been published by our group presenting the longitudinal anatomical and functional changes [16][17][18][19][20][21][22]. All participants completed an extensive battery of neuropsychological tests and underwent brain imaging (functional and structural MRI).…”

“…MRI data were acquired with a 3.0T Philips Ingenia scanner (Philips Medical Systems, Best, the Netherlands). We have described the image acquisition procedure in several of our recent papers; thus, we only shortly reiterate the technical details [1,[17][18][19] (T1-weighted images, recorded with a gradient echo sequence; three-dimensional turbo field echo, 160 sagittal slices, slice thickness = 1mm, in-plane resolution = 1 × 1 mm, field of view = 240 × 240 mm, repetition time = 8.18 ms, echo time = 3.80 ms, flip angle = 8°). FreeSurfer v5.3 (https://surfer.nmr.mgh.harvard.…”

Differences in high-definition transcranial direct current stimulation over the motor hotspot versus the premotor cortex on motor network excitability

“…Moreover, in the participants who did not respond to tDCS, the stimulation site was all over the motor network, including in PMd, with no strong overlap across participants. Again, this suggests that more than the absolute location, differences in brain structure and function 56 should be taken into account to determine the responsiveness of each individual to tDCS. Future studies should determine which stimulation parameters (neural target, nature of stimulation, duration, etc.)…”