Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a fingerprint to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have iden-tified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing com-pelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

Automated summary

The use of human brain connectivity data as a fingerprint for individual identification has become a popular area of research in neuroscience. Recent studies have found that brain signatures can be extracted from resting-state MEG recordings, but their effectiveness in task-related conduct is still uncertain. This study demonstrates that identification improves during tasks compared to resting-state, particularly in controlled tasks, and the brain regions contributing to identification also change during task activities.