Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity

Finn, Emily S; Shen, Xilin; Scheinost, Dustin; Rosenberg, Monica D; Huang, Jessica; Chun, Marvin M; Papademetris, Xenophon; Constable, R Todd

doi:10.1038/nn.4135

Cited by 2,747Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity

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Finn, Emily S, Shen, Xilin, Scheinost, Dustin, Rosenberg, Monica D, Huang, Jessica, Chun, Marvin M, Papademetris, Xenophon, Constable, R Todd·2015·Nature Neuroscience

DOI 10.1038/nn.4135PMC PMC5008686

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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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Individual brains, even among typical subjects, are distinguishable via their functional connectomes.
“individual brains, even among typical subjects, are distinguishable via their functional connectomes (Finn et al., 2015; Laumann et al., 2015; Miranda-Dominguez et al., 2014b).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Finn et al., 2015
Within-subject variability is usually excluded in currently available research.
“Within-subject variability is usually excluded (Finn et al., 2015; Salvatore et al., 2014).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Finn et al., 2015

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Commentary: Functional connectome fingerprint: identifying individuals using patterns of brain connectivity
Biazoli, C. E., Salum, G. A., Pan, P. M., Zugman, A., Amaro, E., Rohde, L. A., Miguel, E. C., Jackowski, A. P., Bressan, R. A., & Sato, J. R. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00047 · Cited by 15 · see citing works
Functional connectome fingerprinting: Identifying individuals and predicting cognitive function via deep learningPreprint
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The quest for identifiability in human functional connectomesPreprint
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Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome
Mantwill, M., Gell, M., Krohn, S., & Finke, C. (2022) · Communications Biology · doi:10.1038/s42003-022-03185-3 · Cited by 43 · see citing works

Authors: Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T.
Affiliations: Yale University (US)
Corresponding author: Finn, Emily S
ORCID: Finn, Emily S
Shen, Xilin
Scheinost, Dustin
Rosenberg, Monica D
Chun, Marvin M
Papademetris, Xenophon
Constable, R Todd
Venue: Nature Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2015-10-12
Volume: 18
Issue: 11
Pages: 1664-1671
ISSN: 1097-6256 (ISSN-L)
1546-1726
DOI: 10.1038/nn.4135
PMC: PMC5008686
OpenAlex: W2111902267
Cited by: 2,747
References: 52
Citations by year: 2026: 225 · 2025: 288 · 2024: 272 · 2023: 300 · 2022: 321 · 2021: 329 · 2020: 306 · 2019: 265 · 2018: 189 · 2017: 156 · 2016: 80 · 2015: 9 · 2014: 4 · 2013: 1
FWCI: 87.05
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Neuroscience (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation (1U54MH091657)
National Institutes of Health (5u54mh091657-05, 1U54‐MH‐091657)
McDonnell Center for Systems Neuroscience (EB009666, 1U54MH091657)
National Institute of Mental Health (1U54 MH091657, U54-MH091657)
National Institute on Drug Abuse (T32DA022975)
National Institute of Biomedical Imaging and Bioengineering (R01EB009666, EB009666)
NIH Blueprint for Neuroscience Research (EB009666, 1U54-MH-091657)
European Social Fund
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: subject identification, frontoparietal network, fluid intelligence, Human Connectome Project
MeSH terms: Adult, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Neural Pathways, Young Adult, Connectome

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