Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

• Published in: Nature neuroscience Vol. 18; no. 11; pp. 1664 - 1671
• Main Authors: Finn, Emily S, Shen, Xilin, Scheinost, Dustin, Rosenberg, Monica D, Huang, Jessica, Chun, Marvin M, Papademetris, Xenophon, Constable, R Todd
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.11.2015; Nature Publishing Group
• Subjects: 59/36; 631/378; 631/378/3920; Adult; Animal Genetics and Genomics; Behavioral Sciences; Biological Techniques; Biomedicine; Brain; Brain - physiology; Brain architecture; Brain Mapping; Brain research; Cognition & reasoning; Cognitive ability; Connectome - methods; Correspondence; Female; Fingerprinting; Fingerprints; Functional magnetic resonance imaging; Functional morphology; Humans; Identification; Individual differences; Intelligence; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Nerve Net - physiology; Neural circuitry; Neural networks; Neural Pathways - physiology; Neurobiology; Neuroimaging; Neurosciences; Young Adult; United States; United States > US; Connecticut
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/nn.4135

This study shows that every individual has a unique pattern of functional connections between brain regions. This functional connectivity profile acts as a ‘fingerprint’ that can accurately identify the individual from a large group. Furthermore, an individual's connectivity profile can predict his or her level of fluid intelligence. Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.