EPFL, 2021-10-18
Media articles:
(EN) "Our brains have a “fingerprint” too"
(FR) "Chaque cerveau possède sa propre empreinte"
Scientific article:
Van De Ville, D. et al (2021). When makes you unique: Temporality of the human brain fingerprint. Science Advances, 7(42). https://doi.org/10.1126/sciadv.abj0751
EU research service - EU CORDIS (Community Research and Development Information Service), 2023-10-21
"Just like fingerprints, we all have a brainprint"
Commentary:
According to the above study, in order to identify an individual optimally, it suffices to obtain a 200-second brain recording, have access to a brain database and conduct a statistical correlation test.
Our fingertip has a distinctive pattern, called "fingerprint" which can be used to identify us. It constitutes a biometric, meaning a biologic measure or a measurable biocharacteristic that allows identification of an individual. In the recent years, high-quality measures of an individual's brain have been obtained allowing characterization of personality traits and behavior.
Neuroscience aims in studying the structure and function of the brain. It seeks to understand what different brain regions do and how they collaborate with each other to mediate specific outcomes. To this purpose, it may establish correlates, meaning mutual relationships or connections between brain regions, which may be either structural or functional. Distant brain regions may be connected structurally, and this via white matter tracts. They may also have similar functional activity patterns, indicating that they are connected functionally. The degree of their functional connection is represented by the term functional connectivity (FC). The complete map of the structural and functional connections in the brain represents the connectome.
Functional connectivity (FC) practically refers to the correlation of functional activity between different brain regions. Studies may determine patterns of coactivation for different brain regions. To this purpose, we obtain brain activity time courses (time series) from different brain regions in the frame of fMRI experiments. Then, statistical correlation is conducted in an effort to find temporal statistical dependencies. In principle, statistical correlation is performed by using a numerical measure called correlation coefficient. Generally, functional connectivity (FC) is modeled and analyzed using network science, which in this case is referred to as brain connectomics.
A most important development in the field was the work of a Yale team [1] in 2015, in the frame of the Human Connectome Project (NIH Blueprint for Neuroscience Research). They were among the first to demonstrate that to a large extent, it is possible to identify an individual's functional connectome from a FC database, simply by computing the connection-wise (Pearson) correlation between the target FC and those in the database [2].
An EPFL team [2] in 2021, demonstrated that an optimal brain fingerprint can be obtained at a time scale of 200 seconds. However, individual "snapshots" are reported to emerge at much shorter time scales.
Which regions appear first and which appear later? The scientists discovered that subcortical regions are the fastest ones for individual identification, while visual and somatomotor regions come right after. Ultimately, at longer time scales, higher-order regions i.e. frontoparietal and default mode network (DMN) emerge.
Both cited studies used data from the HCP database. As mentioned for the first study [*]: "Notably, no brain scans or psychological tests were required"; "instead, the researchers drew upon an unprecedented trove of shared data from more than a thousand subjects made available by the HCP."
[1] Finn, E. S. et al. (2015). Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671. https://doi.org/10.1038/nn.4135
[2] Van De Ville, D. et al (2021). When makes you unique: Temporality of the human brain fingerprint. Science Advances, 7(42). https://doi.org/10.1126/sciadv.abj0751
Media article:
"New Evidence Points to Personal Brain Signatures"
Scientific American, 2016-04-13
"Brain scans of a person doing nothing at all can predict how neural circuits will light up when that same individual is gambling or reading a book"
Scientific article:
"Task-free MRI predicts individual differences in brain activity during task performance"
The following prior study by Finn et al. is cited with the comment that brain networks contain enough information to identify individuals with 99% accuracy: "Brain scans pinpoint individuals from a crowd"
Troubles neurologiques : élucider les mystères des plis du cortex cérébral
CNRS, 2024-11-03