Jason da Silva Castanheira | Montreal Neurological Institute, McGill University
da Silva Castanheira, J., Orozco Perez, H.D., Misic, B. & Baillet, S. Brief segments of neurophysiological activity enable individual differentiation. Nature Communications 12, 5713 (2021). https://doi.org/10.1038/s41467-021-25895-8
Brain fingerprinting: a signature of complex brain signals unique to each individual
We all have the intuition that our brain makes us unique as a person. Recent neuroimaging studies have concluded that patterns of resting-state brain activity can successfully distinguish individuals—yielding the notion of a brain fingerprint. Work published by PhD student Jason da Silva Castanheira and colleagues advances the biological definition of the self as they show that a relatively simple signature of complex brain signals can be derived from short segments of brain activity and is remarkably stable over time. These brain signatures or fingerprints offer the potential to transform neurophysiological phenotyping and population neuroscience.
The researchers aimed to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity recorded from magnetoencephalography (MEG) in 158 participants. They show that neurophysiological measures of functional connectivity (i.e., how different brain regions communicate with one another) can distinguish individuals from a cohort with 90% accuracy and that simpler measures derived from the spatial distribution of neural signal power can similarly differentiate individuals. The researchers verified that typical experimental artifacts such as head motion, heartbeats, and eye blinks in the scanner did not contribute to individual differentiation and showed that the ability to distinguish individuals was uniquely driven by their brain activity and not extraneous factors, including possible unique environmental noise signatures in the lab on the day of their respective visits. Using imaging procedures, da Silva Castanheira and colleagues describe which brain regions produce activity that is the most characteristic to an individual. The strongest finding of this publication is that recordings of brain activity as short as 30 seconds are sufficient to differentiate individuals and that this differentiation is robust across time from recordings taken weeks and months apart.
With the increasing availability of large and open data repositories, new opportunities emerge for researchers to capture and map the neurodiversity of the population in terms of key behaviour, environmental, and clinical variables. This study highlights the unique asset of such datasets where individuals’ brains are scanned at multiple occasions, along with a comprehensive characterization of behavioral and socio-demographic factors. Previous work has shown that mental health disorders may affect the stability of brain fingerprints over the lifespan. The researchers foresee that this present work may inspire future application of quick, safe, and reproducible brain fingerprinting as a tool to assess neurological and mental-health conditions. For instance, future extensions of this work will explore how the stability of brain fingerprints relate to cognitive decline, chronic, neurodegenerative, or acute conditions. One distinctive strength of this contribution is the short durations of task-free brain recordings required to derive an individual brain fingerprint, thereby providing clinicians with a practical tool to investigate individual differences in treatment and disease progression.
Jason da Silva Castanheira
Jason da Silva Castanheira is a PhD student in the laboratory of Dr. Sylvain Baillet at the Montreal Neurological Institute & Hospital, McGill University. He was involved in all aspects of the study, from its conception to its final report with the invaluable aid from Hector Perez, Dr. Sylvain Baillet, and Dr. Bratislav Misic. The researchers, building off the open-source library of the Brainstorm software (also co-developed in the Baillet lab) made the code and the article openly available.
This work was supported by the NIH (R01 EB026299), a Discovery Grant from the Natural Science and Engineering Research Council of Canada (436355-13), the CIHR Canada Research Chair in Neural Dynamics of Brain Systems, the Brain Canada Foundation with support from Health Canada, and the Innovative Ideas program from the Canada First Research Excellence Fund, awarded to Dr. Sylvain Baillet and McGill University for the Healthy Brains for Healthy Lives initiative. This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative. This work was also supported by the Alexander Graham-Bell Doctoral NSERC fellowship awarded to Jason da Silva Castanheira.