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Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity
Open accessAbstract
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 (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
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“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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Metadata
- 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
- Venue
- Nature Neuroscience
- Published
- 2015-10-12
- Volume
- 18
- Issue
- 11
- Pages
- 1664-1671
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- 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
- FWCI
- 87.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- 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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