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A patterned fingerprint of the brain

A patterned fingerprint of the brain

Studying multiple neural networks, researchers from EPFL Switzerland found that every one of us has a unique brain fingerprint. Comparing the graphs generated from MRI scans of the same subjects taken a few days apart, they were able to correctly match up the two scans of a given subject nearly 95% of the time. After creating a modeling technique, they were able to create a unique signature of each individual’s studied brain, signatures that changes over the course of our lives.

Physician Marcello Malpighi discovered different patterns of ridges and sweat glands on fingertips in the 17th century. This pivotal discovery sparked a decades-long search for ways to uniquely identify people using their fingerprints, a technique that is still widely employed today. The concept of fingerprinting has evolved over time to include other biometric data such as voice recordings and retinal scans, among other things.

Only in the last several years have technologies and methodology enabled high-quality brain measurements to the point where personality traits and behavior can now be classified.

Until now, neuroscientists have identified brain fingerprints using two MRI scans taken over a fairly long period. But do the fingerprints actually appear after just five seconds, for example, or do they need longer? And what if fingerprints of different brain areas appeared at different moments in time? Nobody knew the answer. So, we tested different time scales to see what would happen.

Enrico Amico, EPFL’s Medical Image Processing Laboratory and the EPFL Center for Neuroprosthetics

Exploring dynamic brain fingerprints. Schematic of dynamic connectome identification for one subject. First, the time scale (window length) of the exploration, here depicted as a gradient cone, is set; second, dynamic FC frames are computed at each window for both test and retest fMRI data; finally, the best matching frames across test and retest data are retrieved for identification. © EPFL
Brain fingerprint resides in few FC frames. (A) Evaluation of brain fingerprints across temporal scales through dlself (left), dlothers (middle), and dldiff scores (right), when ranking dFC frames based on individual dIself, in descending order. The dIdiff scores obtained with the ranking are compared with the ones obtained when taking all frames (triangles), also depicted in Fig. 2. (B) Edgewise SD across subjects of the best matching dFC frames at each temporal scale. The matrices are ordered according to the seven resting-state subnetwork organization proposed by Yeo and colleagues (59), specifically visual (VIS), somatomotor (SM), dorsal attention (DA), ventral attention (VA), limbic (L), frontoparietal (FP), and default mode network (DMN). For completeness, an eighth subcortical subnetwork (S) was added at the end (see the “Brain atlas” section in Materials and Methods for details). (C) Edgewise median across subjects of the best matching (i.e., k = 1) dFC frames at each temporal scale.

His research team discovered that seven seconds was insufficient for detecting relevant data, while 1 minute and 40 seconds were. They also revealed that the sensory parts of the brain, particularly those associated with eye movement, visual perception, and visual attention, produce the fastest brain fingerprints. With the passage of time, frontal cortical regions linked with more complex cognitive activities begin to expose unique information to each of us.

Also, the fingerprints are linked to time scales of functional brain connections and may be linked to short bursts of brain activity. Based on these first findings, this work appears promising and provides a step toward a deeper understanding of what and when makes our brains unique.

When makes you unique: Temporality of the human brain fingerprint, Dimitri Van De Ville, Younes Farouj, Maria Giulia Preti, Raphael Liegeois, Enrico Amico

Published: October 2021
DOI: 10.1126/sciadv.abj0751

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