Functional Connectome Fingerprinting: Identifying Individuals Using Patterns of Brain Connectivity

Overview

Journal Nat Neurosci

Date 2015 Oct 13

PMID 26457551

Citations 1130

Authors

Emily S Finn

Xilin Shen

Dustin Scheinost

Monica D Rosenberg

Jessica Huang

Marvin M Chun

Xenophon Papademetris

R Todd Constable

Affiliations

Soon will be listed here.

Abstract

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.

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References

Barch D, Burgess G, Harms M, Petersen S, Schlaggar B, Corbetta M . Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage. 2013; 80:169-89. PMC: 4011498. DOI: 10.1016/j.neuroimage.2013.05.033. View

Grabner R, Ansari D, Reishofer G, Stern E, Ebner F, Neuper C . Individual differences in mathematical competence predict parietal brain activation during mental calculation. Neuroimage. 2007; 38(2):346-56. DOI: 10.1016/j.neuroimage.2007.07.041. View

Laumann T, Gordon E, Adeyemo B, Snyder A, Joo S, Chen M . Functional System and Areal Organization of a Highly Sampled Individual Human Brain. Neuron. 2015; 87(3):657-70. PMC: 4642864. DOI: 10.1016/j.neuron.2015.06.037. View

Van Essen D, Smith S, Barch D, Behrens T, Yacoub E, Ugurbil K . The WU-Minn Human Connectome Project: an overview. Neuroimage. 2013; 80:62-79. PMC: 3724347. DOI: 10.1016/j.neuroimage.2013.05.041. View

Glasser M, Sotiropoulos S, Wilson J, Coalson T, Fischl B, Andersson J . The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013; 80:105-24. PMC: 3720813. DOI: 10.1016/j.neuroimage.2013.04.127. View

Bilker W, Hansen J, Brensinger C, Richard J, Gur R, Gur R . Development of abbreviated nine-item forms of the Raven's standard progressive matrices test. Assessment. 2012; 19(3):354-69. PMC: 4410094. DOI: 10.1177/1073191112446655. View

Mueller S, Wang D, Fox M, Yeo B, Sepulcre J, Sabuncu M . Individual variability in functional connectivity architecture of the human brain. Neuron. 2013; 77(3):586-95. PMC: 3746075. DOI: 10.1016/j.neuron.2012.12.028. View

Cuthbert B, Insel T . Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 2013; 11:126. PMC: 3653747. DOI: 10.1186/1741-7015-11-126. View

Gabrieli J, Ghosh S, Whitfield-Gabrieli S . Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron. 2015; 85(1):11-26. PMC: 4287988. DOI: 10.1016/j.neuron.2014.10.047. View

10.

Castellanos F, Di Martino A, Craddock R, Mehta A, Milham M . Clinical applications of the functional connectome. Neuroimage. 2013; 80:527-40. PMC: 3809093. DOI: 10.1016/j.neuroimage.2013.04.083. View

11.

Zilles K, Armstrong E, Schleicher A, KRETSCHMANN H . The human pattern of gyrification in the cerebral cortex. Anat Embryol (Berl). 1988; 179(2):173-9. DOI: 10.1007/BF00304699. View

12.

Van Dijk K, Sabuncu M, Buckner R . The influence of head motion on intrinsic functional connectivity MRI. Neuroimage. 2011; 59(1):431-8. PMC: 3683830. DOI: 10.1016/j.neuroimage.2011.07.044. View

13.

Hampson M, Tokoglu F, Shen X, Scheinost D, Papademetris X, Constable R . Intrinsic brain connectivity related to age in young and middle aged adults. PLoS One. 2012; 7(9):e44067. PMC: 3439483. DOI: 10.1371/journal.pone.0044067. View

14.

Rypma B, DEsposito M . The roles of prefrontal brain regions in components of working memory: effects of memory load and individual differences. Proc Natl Acad Sci U S A. 1999; 96(11):6558-63. PMC: 26921. DOI: 10.1073/pnas.96.11.6558. View

15.

Hill J, Dierker D, Neil J, Inder T, Knutsen A, Harwell J . A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. J Neurosci. 2010; 30(6):2268-76. PMC: 2836191. DOI: 10.1523/JNEUROSCI.4682-09.2010. View

16.

Newman S, Carpenter P, Varma S, Just M . Frontal and parietal participation in problem solving in the Tower of London: fMRI and computational modeling of planning and high-level perception. Neuropsychologia. 2003; 41(12):1668-82. DOI: 10.1016/s0028-3932(03)00091-5. View

17.

Jiang T, He Y, Zang Y, Weng X . Modulation of functional connectivity during the resting state and the motor task. Hum Brain Mapp. 2004; 22(1):63-71. PMC: 6871844. DOI: 10.1002/hbm.20012. View

18.

Power J, Cohen A, Nelson S, Wig G, Barnes K, Church J . Functional network organization of the human brain. Neuron. 2011; 72(4):665-78. PMC: 3222858. DOI: 10.1016/j.neuron.2011.09.006. View

19.

Hutchison R, Womelsdorf T, Allen E, Bandettini P, Calhoun V, Corbetta M . Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage. 2013; 80:360-78. PMC: 3807588. DOI: 10.1016/j.neuroimage.2013.05.079. View

20.

Joshi A, Scheinost D, Okuda H, Belhachemi D, Murphy I, Staib L . Unified framework for development, deployment and robust testing of neuroimaging algorithms. Neuroinformatics. 2011; 9(1):69-84. PMC: 3066099. DOI: 10.1007/s12021-010-9092-8. View