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bioRxiv · 10.1101/2020.06.18.159913

Brainprint: identifying individuals from Magnetoencephalography

Wu, S.·Ramdas, A.·Wehbe, L.
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Abstract

Magnetoencephalography (MEG) is used to study a wide variety of cognitive processes. Increasingly, researchers are adopting principles of open science and releasing their MEG data. While essential for reproducibility, sharing MEG data has unforeseen privacy risks. Individual differences may make a participant identifiable from their anonymized recordings. However, our ability to identify individuals based on these individual differences has not yet been assessed. Here, we propose interpretable MEG features to characterize individual difference. We term these features brainprints (brain fingerprints). We show through several datasets that brainprints accurately identify individuals across days, tasks, and even between MEG and Electroencephalography (EEG). Furthermore, we identify consistent brainprint components that are important for identification. We study the dependence of identifiability on the amount of data available. We also relate identifiability to the level of preprocessing, the experimental task. Our findings reveal specific aspects of individual variability in MEG. They also raise concerns about unregulated sharing of brain data, even if anonymized. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/159913v3_fig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@50c58corg.highwire.dtl.DTLVardef@d99cc1org.highwire.dtl.DTLVardef@c255cdorg.highwire.dtl.DTLVardef@8f2c13_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Graphical abstract. Identifying which subject a segment of MEG data belongs to is strikingly easy when other data from the same session is available for every subject. We propose three types of interpretable features that can also be used to identify individuals across sessions with high accuracy. Identifiability of individuals is influenced by factors such as resting state vs. task state, components of each feature, the sample size and the level of preprocessing. Our results reveal aspects of individual variability in MEG signals and highlight privacy risks associated with MEG data sharing. C_FIG

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Wu, S., Ramdas, A., Wehbe, L.. 2020-06-20. Brainprint: identifying individuals from Magnetoencephalography. https://doi.org/10.1101/2020.06.18.159913

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