Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states
Nigel Colenbier,
Ekansh Sareen,
Tamara del-Aguila Puntas,
Alessandra Griffa,
Giovanni Pellegrino,
Dante Mantini,
Daniele Marinazzo,
Giorgio Arcara,
Enrico Amico
Affiliations
Nigel Colenbier
IRCCS San Camillo Hospital, Venice, Italy
Ekansh Sareen
Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
Tamara del-Aguila Puntas
Laboratorio de Psicobiologia, Departmento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Spain
Alessandra Griffa
Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
Giovanni Pellegrino
IRCCS San Camillo Hospital, Venice, Italy
Dante Mantini
Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
Daniele Marinazzo
Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
Giorgio Arcara
IRCCS San Camillo Hospital, Venice, Italy
Enrico Amico
Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Corresponding author.
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 identified 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 compelling 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.
