Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

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...

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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
Online Access	Get full text
ISSN	1097-6256 1546-1726
DOI	10.1038/nn.4135

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Abstract	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.
AbstractList	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. 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. 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. 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.
Audience	Academic
Author	Chun, Marvin M Huang, Jessica Papademetris, Xenophon Rosenberg, Monica D Shen, Xilin Constable, R Todd Scheinost, Dustin Finn, Emily S
Author_xml	– sequence: 1 givenname: Emily S surname: Finn fullname: Finn, Emily S email: emily.finn@yale.edu organization: Interdepartmental Neuroscience Program, Yale University – sequence: 2 givenname: Xilin surname: Shen fullname: Shen, Xilin organization: Department of Diagnostic Radiology, Yale School of Medicine – sequence: 3 givenname: Dustin surname: Scheinost fullname: Scheinost, Dustin organization: Department of Diagnostic Radiology, Yale School of Medicine – sequence: 4 givenname: Monica D surname: Rosenberg fullname: Rosenberg, Monica D organization: Department of Psychology, Yale University – sequence: 5 givenname: Jessica surname: Huang fullname: Huang, Jessica organization: Department of Diagnostic Radiology, Yale School of Medicine – sequence: 6 givenname: Marvin M surname: Chun fullname: Chun, Marvin M organization: Interdepartmental Neuroscience Program, Yale University, Department of Psychology, Yale University, Department of Neurobiology, Yale University – sequence: 7 givenname: Xenophon surname: Papademetris fullname: Papademetris, Xenophon organization: Department of Diagnostic Radiology, Yale School of Medicine, Department of Biomedical Engineering, Yale University – sequence: 8 givenname: R Todd surname: Constable fullname: Constable, R Todd organization: Interdepartmental Neuroscience Program, Yale University, Department of Diagnostic Radiology, Yale School of Medicine, Department of Neurosurgery, Yale School of Medicine
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26457551$$D View this record in MEDLINE/PubMed
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Snippet	This study shows that every individual has a unique pattern of functional connections between brain regions. This functional connectivity profile acts as a... Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals....
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SubjectTerms	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
Title	Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity
URI	https://link.springer.com/article/10.1038/nn.4135 https://www.ncbi.nlm.nih.gov/pubmed/26457551 https://www.proquest.com/docview/1766292747 https://www.proquest.com/docview/2565728438 https://www.proquest.com/docview/1728258610 https://www.proquest.com/docview/1773837366
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