Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity

While fMRI studies typically collapse data from many subjects, 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...

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Main Authors:	Finn, Emily S., Shen, Xilin, Scheinost, Dustin, Rosenberg, Monica D., Huang, Jessica, Chun, Marvin M., Papademetris, Xenophon, Constable, R. Todd
Format:	Online
Language:	English
Published:	2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008686/

id	pubmed-5008686
recordtype	oai_dc
spelling	pubmed-50086862016-09-01 Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity Finn, Emily S. Shen, Xilin Scheinost, Dustin Rosenberg, Monica D. Huang, Jessica Chun, Marvin M. Papademetris, Xenophon Constable, R. Todd Article While fMRI studies typically collapse data from many subjects, 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 notably, 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 based on functional connectivity fMRI. 2015-10-12 2015-11 /pmc/articles/PMC5008686/ /pubmed/26457551 http://dx.doi.org/10.1038/nn.4135 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Finn, Emily S. Shen, Xilin Scheinost, Dustin Rosenberg, Monica D. Huang, Jessica Chun, Marvin M. Papademetris, Xenophon Constable, R. Todd
spellingShingle	Finn, Emily S. Shen, Xilin Scheinost, Dustin Rosenberg, Monica D. Huang, Jessica Chun, Marvin M. Papademetris, Xenophon Constable, R. Todd Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity
author_facet	Finn, Emily S. Shen, Xilin Scheinost, Dustin Rosenberg, Monica D. Huang, Jessica Chun, Marvin M. Papademetris, Xenophon Constable, R. Todd
author_sort	Finn, Emily S.
title	Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity
title_short	Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity
title_full	Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity
title_fullStr	Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity
title_full_unstemmed	Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity
title_sort	functional connectome fingerprinting: identifying individuals based on patterns of brain connectivity
description	While fMRI studies typically collapse data from many subjects, 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 notably, 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 based on functional connectivity fMRI.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008686/
_version_	1613641650434736128

Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity

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