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

The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

Open access

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

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/nn.4135

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Research domain criteria (RDoC): toward a new classification framework for research on mental disorders
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091379 · Cited by 7,067
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
The minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
Automatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124
Toward the future of psychiatric diagnosis: the seven pillars of RDoC
Cuthbert, B. N., & Insel, T. R. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-126 · Cited by 3,053

Flagged first, then by citations

Show all 52 references

1 of the 52 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

Cattell, R.B. Intelligence: Its Structure, Growth and Action: Its Structure, Growth and Action (Elsevier, 1987).

Sorted by citations

Items 201–300 of 1,007

Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation
Mithani, K., Mikhail, M., Morgan, B. R., Wong, S. M., Weil, A. G., Deschênes, S., Wang, S., Bernal, B., Guillen, M. R., Ochi, A., et al. (2019) · Annals of Neurology · doi:10.1002/ana.25574 · Cited by 107
The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions
O’Connor, D. A., Potler, N. V., Kovacs, M., Xu, T., Ai, L., Pellman, J., Vanderwal, T., Parra, L. C., Cohen, S. S., Ghosh, S., et al. (2017) · GigaScience · doi:10.1093/gigascience/giw011 · Cited by 100
Connectome-based Models Predict Separable Components of Attention in Novel Individuals
Rosenberg, M. D., Hsu, T. W., Scheinost, D., Constable, R. T., & Chun, M. M. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01197 · Cited by 103
Self-citation
Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease
Lin, Q., Rosenberg, M. D., Yoo, K., Hsu, T. W., O’Connell, T. P., & Chun, M. M. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00094 · Cited by 104
Self-citation
Shared and Distinct Functional Architectures of Brain Networks Across Psychiatric Disorders
Xia, M., Womer, F. Y., Chang, M., Zhu, Y., Zhou, Q., Edmiston, E. K., Jiang, X., Wei, S., Duan, J., Xu, K., et al. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby046 · Cited by 103
Anatomy and white-matter connections of the precuneus
Tanglay, O., Young, I. M., Dadario, N. B., Briggs, R. G., Fonseka, R. D., Dhanaraj, V., Hormovas, J., Lin, Y., & Sughrue, M. E. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00529-1 · Cited by 101
Behavioral and Neural Signatures of Working Memory in Childhood
Rosenberg, M. D., Martinez, S. A., Rapuano, K. M., Conley, M. I., Cohen, A. O., Cornejo, M. D., Hagler, D. J., Meredith, W. J., Anderson, K. M., Wager, T. D., et al. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2841-19.2020 · Cited by 98
Self-citation
Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI
Nentwich, M., Ai, L., Madsen, J., Telesford, Q. K., Haufe, S., Milham, M. P., & Parra, L. C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117001 · Cited by 104
The social nature of mitochondria: Implications for human health
Picard, M. A., & Sandi, C. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.04.017 · Cited by 107
The nonhuman primate neuroimaging and neuroanatomy project
Hayashi, T., Hou, Y., Glasser, M. F., Autio, J. A., Knoblauch, K., Inoue‐Murayama, M., Coalson, T., Yacoub, E. S., Smith, S. M., Kennedy, H., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117726 · Cited by 110
Connectome-Based Predictive Modeling of Individual Anxiety
Wang, Z., Goerlich, K. S., Ai, H., Aleman, A., Luo, Y., & Xu, P. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa407 · Cited by 105
Brain structure-function coupling provides signatures for task decoding and individual fingerprinting
Griffa, A., Amico, E., Liégeois, R., Ville, D. V. D., & Preti, M. G. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118970 · Cited by 98
Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease
Finn, E. S., & Constable, R. T. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.3/efinn · Cited by 101
Self-citation
Brain signal variability is modulated as a function of internal and external demand in younger and older adults
Grady, C. L., & Garrett, D. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.031 · Cited by 103
Edges in brain networks: Contributions to models of structure and function
Faskowitz, J., Betzel, R. F., & Sporns, O. (2021) · Network Neuroscience · doi:10.1162/netn_a_00204 · Cited by 110
Brief segments of neurophysiological activity enable individual differentiation
da Silva Castanheira, J., Orozco Perez, H. D., Misic, B., & Baillet, S. (2021) · Nature Communications · doi:10.1038/s41467-021-25895-8 · Cited by 101
Meta-matching as a simple framework to translate phenotypic predictive models from big to small data
He, T., An, L., Chen, P., Chen, J., Feng, J., Bzdok, D., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01059-9 · Cited by 101
Correspondence between evoked and intrinsic functional brain network configurations
Bolt, T., Nomi, J. S., Rubinov, M., & Uddin, L. Q. (2017) · Human Brain Mapping · doi:10.1002/hbm.23500 · Cited by 104
Relationships between Human Brain Structural Connectomes and Traits
Zhang, Z., Allen, G. I., Hongtu Zhu, B., & Dunson, D. B. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.027 · Cited by 98
Social cognition in context: A naturalistic imaging approach
Redcay, E., & Moraczewski, D. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116392 · Cited by 100
Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume
Cui, Z., Su, M., Li, L., Shu, H., & Gong, G. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx061 · Cited by 96
Prediction of stimulus-independent and task-unrelated thought from functional brain networks
Kucyi, A., Esterman, M. S., Capella, J., Green, A., Uchida, M., Biederman, J., Gabrieli, J. D. E., Valera, E. M., & Whitfield, S. (2021) · Nature Communications · doi:10.1038/s41467-021-22027-0 · Cited by 99
The Neurobiological Effects of Electroconvulsive Therapy Studied Through Magnetic Resonance: What Have We Learned, and Where Do We Go?
Ousdal, O. T., Brancati, G. E., Keßler, U., Erchinger, V. J., Dale, A. M., Abbott, C., & Oltedal, L. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.05.023 · Cited by 106
A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex Using Intrinsic Individual Brain Connectivity
Menon, S. S., & Krishnamurthy, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-42090-4 · Cited by 95
Addressing the reliability fallacy in fMRI: Similar group effects may arise from unreliable individual effects
Fröhner, J. H., Teckentrup, V., Smolka, M. N., & Kroemer, N. B. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.053 · Cited by 92
Comparing functional connectivity matrices: A geometry-aware approach applied to participant identification
Venkatesh, M., JáJá, J. F., & Pessoa, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116398 · Cited by 98
Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals
Avery, E. W., Yoo, K., Rosenberg, M. D., Greene, A. S., Gao, S., Na, D. L., Scheinost, D., Constable, R. T., & Chun, M. M. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01487 · Cited by 96
Self-citation
Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks
Akiki, T. J., & Abdallah, C. G. (2019) · Scientific Reports · doi:10.1038/s41598-019-55738-y · Cited by 95
Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series
Sporns, O., Faskowitz, J., Teixeira, A. S., Cutts, S. A., & Betzel, R. F. (2021) · Network Neuroscience · doi:10.1162/netn_a_00182 · Cited by 98
Frequency dependent hub role of the dorsal and ventral right anterior insula
Wang, Y., Zhu, L., Zou, Q., Cui, Q., Liao, W., Duan, X., Biswal, B. B., & Chen, H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.004 · Cited by 95
Behavioural relevance of spontaneous, transient brain network interactions in fMRI
Vidaurre, D., Llera, A., Smith, S. M., & Woolrich, M. W. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117713 · Cited by 101
Federated Multi-Task Learning for Joint Diagnosis of Multiple Mental Disorders on MRI Scans
Huang, Z.-A., Hu, Y. F., Liu, R., Xue, X., Zhu, Z., Song, L., & Tan, K. C. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3210940 · Cited by 102
Amyloid-associated hyperconnectivity drives tau spread across connected brain regions in Alzheimer’s disease
Roemer, S. N., Wagner, F., Evangelista, L., Rauchmann, B., Dehsarvi, A., Steward, A., Dewenter, A., Biel, D., Zhu, Z., Pescoller, J., et al. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adp2564 · Cited by 99
Graph convolutional network for fMRI analysis based on connectivity neighborhood
Wang, L., Li, K., & Hu, X. P. (2020) · Network Neuroscience · doi:10.1162/netn_a_00171 · Cited by 91
Varying demands for cognitive control reveals shared neural processes supporting semantic and episodic memory retrieval
Vatansever, D., Smallwood, J., & Jefferies, E. (2021) · Nature Communications · doi:10.1038/s41467-021-22443-2 · Cited by 100
Toward personalized medicine in connectomic deep brain stimulation
Hollunder, B., Rajamani, N., Siddiqi, S., Finke, C., Kühn, A. A., Mayberg, H. S., Fox, M., Neudorfer, C., & Horn, A. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102211 · Cited by 89
Earlier Alzheimer’s disease onset is associated with tau pathology in brain hub regions and facilitated tau spreading
Frontzkowski, L. K., Ewers, M., Brendel, M., Biel, D., Ossenkoppele, R., Hager, P., Steward, A., Dewenter, A., Roemer, S. N., Rubinski, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32592-7 · Cited by 100
Investigations into within- and between-subject resting-state amplitude variations
Bijsterbosch, J. D., Harrison, S. J., Duff, E. P., Alfaro‐Almagro, F., Woolrich, M. W., & Smith, S. M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.014 · Cited by 84
Parcellation influence on the connectivity‐based structure–function relationship in the human brain
Messé, A. (2019) · Human Brain Mapping · doi:10.1002/hbm.24866 · Cited by 90
Towards an Individual Differences Perspective in Mindfulness Training Research: Theoretical and Empirical Considerations
Tang, R., & Braver, T. S. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00818 · Cited by 91
Hippocampal seed connectome-based modeling predicts the feeling of stress
Goldfarb, E. V., Rosenberg, M. D., Seo, D., Constable, R. T., & Sinha, R. (2020) · Nature Communications · doi:10.1038/s41467-020-16492-2 · Cited by 93
Self-citation
Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity
Kucyi, A., Tambini, A., Sadaghiani, S., Keilholz, S. D., & Cohen, J. R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00037 · Cited by 95
Overview of leakage scenarios in supervised machine learning
Sasse, L., Nicolaisen‐Sobesky, E., Dukart, J., Eickhoff, S. B., GOETZ, M. A., Hamdan, S., Komeyer, V., Kulkarni, A. J., Lahnakoski, J. M., Love, B. C., et al. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01193-8 · Cited by 134
Resting-state fMRI correlations: From link-wise unreliability to whole brain stability
Pannunzi, M., Hindriks, R., Bettinardi, R. G., Wenger, E., Lisofsky, N., Mårtensson, J., Butler, O., Filevich, E., Becker, M., Lochstet, M., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.006 · Cited by 93
Connectome-based individualized prediction of loneliness
Feng, C., Wang, L., Li, T., & Xu, P. (2019) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsz020 · Cited by 90
A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity
Yamashita, M., Yoshihara, Y., Hashimoto, R., Yahata, N., Ichikawa, N., Sakai, Y., Yamada, T., Matsukawa, N., Okada, G., Tanaka, S., et al. (2018) · eLife · doi:10.7554/elife.38844 · Cited by 92
Reliability and Individual Specificity of EEG Microstate Characteristics
Liu, J., Xu, J., Zou, G., Yong He, A., Zou, Q., & Gao, J. (2020) · Brain Topography · doi:10.1007/s10548-020-00777-2 · Cited by 93
Functional neuroimaging in psychiatry and the case for failing better
Nour, M. M., Liu, Y., & Dolan, R. J. (2022) · Neuron · doi:10.1016/j.neuron.2022.07.005 · Cited by 92
Brain functional connectivity correlates of coping styles
Santarnecchi, E., Sprugnoli, G., Tatti, E., Mencarelli, L., Neri, F., Momi, D., Di Lorenzo, G., Pascual-Leone, A., Rossi, S., & Rossi, A. (2018) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-018-0583-7 · Cited by 85
Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study
Santarnecchi, E., Emmendorfer, A. K., & Pascual‐Leone, Á. (2017) · Intelligence · doi:10.1016/j.intell.2017.04.008 · Cited by 95
Individual structural features constrain the mouse functional connectome
Melozzi, F., Bergmann, E., Harris, J. A., Kahn, I., Jirsa, V., & Bernard, C. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906694116 · Cited by 89
The early origins and the growing popularity of the individual-subject analytic approach in human neuroscience
Fedorenko, E. G. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.023 · Cited by 84
The promise of a model-based psychiatry: building computational models of mental ill health
Hauser, T. U., Skvortsova, V., Choudhury, M. D., & Koutsouleris, N. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00152-2 · Cited by 97
Longer scans boost prediction and cut costs in brain-wide association studies
Ooi, L. Q. R., Orbán, C., Zhang, S., Nichols, T. E., Tan, T. W. K., Kong, R., Marek, S. A., Dosenbach, N. U. F., Laumann, T. O., Gordon, E. M., et al. (2025) · Nature · doi:10.1038/s41586-025-09250-1 · Cited by 94
The Myth of Optimality in Clinical Neuroscience
Holmes, A. J., & Patrick, L. M. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.12.006 · Cited by 89
Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors
Yoo, K., Rosenberg, M. D., Noble, S., Scheinost, D., Constable, R. T., & Chun, M. M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.060 · Cited by 92
Self-citation
Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes
Finn, E. S., & Rosenberg, M. D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118254 · Cited by 89
Self-citation
The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis
Reinen, J., Chén, O. Y., Hutchison, R. M., Yeo, B. T. T., Anderson, K. M., Sabuncu, M. R., Öngür, D., Roffman, J. L., Smoller, J. W., Baker, J. T., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03462-y · Cited by 84
Functional connectomics from a “big data” perspective
Xia, M., & Yong He, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.031 · Cited by 85
Enhanced estimations of post‐stroke aphasia severity using stacked multimodal predictions
Pustina, D., Coslett, H. B., Ungar, L., Faseyitan, O. K., Medaglia, J. D., Avants, B., & Schwartz, M. F. (2017) · Human Brain Mapping · doi:10.1002/hbm.23752 · Cited by 86
Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence
Assem, M., Blank, I., Mineroff, Z., Ademoğlu, A., & Fedorenko, E. G. (2020) · Cortex · doi:10.1016/j.cortex.2020.06.013 · Cited by 92
Hard to wake up? The cerebral correlates of sleep inertia assessed using combined behavioral, EEG and fMRI measures
Vallat, R., Meunier, D., Nicolas, A., & Ruby, P. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.033 · Cited by 84
Temporal stability of functional brain modules associated with human intelligence
Hilger, K., Fukushima, M., Sporns, O., & Fiebach, C. J. (2019) · Human Brain Mapping · doi:10.1002/hbm.24807 · Cited by 100
Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling
Lichenstein, S. D., Scheinost, D., Potenza, M. N., Carroll, K. M., & Yip, S. W. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0586-y · Cited by 84
Self-citation
How Tasks Change Whole-Brain Functional Organization to Reveal Brain-Phenotype Relationships
Greene, A. S., Gao, S., Noble, S., Scheinost, D., & Constable, R. T. (2019) · Cell Reports · doi:10.1016/j.celrep.2020.108066 · Cited by 82
Self-citation
White Matter Functional Connectivity in Resting-State fMRI: Robustness, Reliability, and Relationships to Gray Matter
Wang, P., Wang, J., Michael, A., Wang, Z., Klugah‐Brown, B., Meng, C., & Biswal, B. B. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab181 · Cited by 81
Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity
Yan, X., Kong, R., Xue, A., Yang, Q., Orbán, C., An, L., Holmes, A. J., Qian, X., Chen, J., Zuo, X., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120010 · Cited by 86
Connectome biomarkers of drug‐resistant epilepsy
Larivière, S., Bernasconi, A., Bernasconi, N., & Bernhardt, B. C. (2020) · Epilepsia · doi:10.1111/epi.16753 · Cited by 83
Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation
Momi, D., Ozdemir, R. A., Tadayon, E., Boucher, P. O., Shafi, M. M., Pascual‐Leone, Á., & Santarnecchi, E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117698 · Cited by 83
Decision-making ability, psychopathology, and brain connectivity
Moutoussis, M., Garzón, B., Neufeld, S., Bach, D. R., Rigoli, F., Goodyer, I. M., Bullmore, E. T., Fonagy, P., Jones, P. B., Hauser, T. U., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.04.019 · Cited by 88
Neonatal brain injury and aberrant connectivity
Smyser, C. D., Wheelock, M. D., Limbrick, D. D., & Neil, J. J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.057 · Cited by 87
Test-retest reliability of dynamic functional connectivity in resting state fMRI
Zhang, C., Baum, S. A., Adduru, V. R., Biswal, B. B., & Michael, A. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.021 · Cited by 84
An Essential Role of the Intraparietal Sulcus in Response Inhibition Predicted by Parcellation-Based Network
Osada, T., Ohta, S., Ogawa, A., Tanaka, M., Suda, A., Kamagata, K., Hori, M., Aoki, S., Shimo, Y., Hattori, N., et al. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2244-18.2019 · Cited by 90
EEG fingerprinting: subject-specific signature based on the aperiodic component of power spectrum
Demuru, M., & Fraschini, M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103748 · Cited by 82
Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior
Santoro, A., Battiston, F., Lucas, M., Petri, G., & Amico, E. (2024) · Nature Communications · doi:10.1038/s41467-024-54472-y · Cited by 100
Connectome-based individualized prediction of temperament trait scores
Jiang, R., Calhoun, V. D., Zuo, N., Lin, D., Li, J., Fan, L., Qi, S., Sun, H., Fu, Z., Song, M., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.038 · Cited by 82
Relationships between depressive symptoms and brain responses during emotional movie viewing emerge in adolescence
Gruskin, D. C., Rosenberg, M. D., & Holmes, A. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116217 · Cited by 86
Self-citation
Which multiband factor should you choose for your resting-state fMRI study?
Risk, B. B., Murden, R., Wu, J., Nebel, M. B., Venkataraman, A., Zhang, Z., & Qiu, D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117965 · Cited by 82
Functional Connectivity of the Nucleus Accumbens and Changes in Appetite in Patients With Depression
Kroemer, N. B., Opel, N., Teckentrup, V., Li, M., Grotegerd, D., Meinert, S., Lemke, H., Kircher, T. T. J., Nenadić, I., Krug, A., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.2464 · Cited by 89
Heritability of individualized cortical network topography
Anderson, K. M., Ge, T., Kong, R., Patrick, L. M., Spreng, R. N., Sabuncu, M. R., Yeo, B. T. T., & Holmes, A. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016271118 · Cited by 90
Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations
Sareen, E., Zahar, S., Ville, D. V. D., Gupta, A., Griffa, A., & Amico, E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118331 · Cited by 78
Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics
Luppi, A. I., Gellersen, H. M., Liu, Z.-Q., Peattie, A. R. D., Manktelow, A. E., Adapa, R., Owen, A. M., Naçi, L., Menon, D. K., Dimitriadis, S. I., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48781-5 · Cited by 86
Families that fire together smile together: Resting state connectome similarity and daily emotional synchrony in parent-child dyads
Lee, T., Miernicki, M. E., & Telzer, E. H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.078 · Cited by 85
Graph Theoretic Analysis of Resting State Functional MR Imaging
Medaglia, J. D. (2017) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2017.06.008 · Cited by 82
Individual variability in brain representations of pain
Kohoutová, L., Atlas, L. Y., Büchel, C., Buhle, J. T., Geuter, S., Jepma, M., Koban, L., Krishnan, A., Lee, D. H., Lee, S., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01081-x · Cited by 79
Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability
Xu, T., Opitz, A., Craddock, R. C., Wright, M. J., Zuo, X., & Milham, M. P. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw241 · Cited by 80
Functional connectivity within and between intrinsic brain networks correlates with trait mind wandering
Godwin, C. A., Hunter, M. A., Bezdek, M. A., Lieberman, G., Elkin-Frankston, S., Romero, V. L., Witkiewitz, K. A., Clark, V. P., & Schumacher, E. H. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.07.006 · Cited by 86
Individualized prediction of trait narcissism from whole‐brain resting‐state functional connectivity
Feng, C., Yuan, J., Geng, H., Gu, R., Zhou, H., Wu, X., & Luo, Y. (2018) · Human Brain Mapping · doi:10.1002/hbm.24205 · Cited by 78
A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity
Takagi, Y., Sakai, Y., Abe, Y., Nishida, S., Harrison, B. J., Martínez‐Zalacaín, I., Soriano‐Mas, C., Narumoto, J., & Tanaka, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.080 · Cited by 87
Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research
Yip, S. W., Kiluk, B. D., & Scheinost, D. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.11.001 · Cited by 77
Self-citation
Robust prediction of individual personality from brain functional connectome
Cai, H., Zhu, J., & Yu, Y. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa044 · Cited by 81
Characterizing the functional connectivity diversity of the insula cortex: Subregions, diversity curves and behavior
Tian, Y., & Zalesky, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.055 · Cited by 80
Testing a Cognitive Control Model of Human Intelligence
Chen, Y., Spagna, A., Wu, T., Kim, T. H., Wu, Q., Chen, C., Wu, Y., & Fan, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-39685-2 · Cited by 80
Patterns of sociocognitive stratification and perinatal risk in the child brain
Alnæs, D., Kaufmann, T., Marquand, A. F., Smith, S. M., & Westlye, L. T. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001517117 · Cited by 75
Effect of Spatial Smoothing on Task fMRI ICA and Functional Connectivity
Chen, Z., & Calhoun, V. D. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00015 · Cited by 86
A Connectomic Hypothesis for the Hominization of the Brain
Changeux, J., Goulas, A., & Hilgetag, C. C. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa365 · Cited by 80
Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability
Rasero, J., Sentis, A. I., Yeh, F.-C. F., & Verstynen, T. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008347 · Cited by 78
The history and future of resting-state functional magnetic resonance imaging
Biswal, B. B., & Uddin, L. Q. (2025) · Nature · doi:10.1038/s41586-025-08953-9 · Cited by 87
Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI
Airan, R. D., Vogelstein, J. T., Pillai, J. J., Caffo, B. S., Pekar, J. J., & Sair, H. I. (2016) · Human Brain Mapping · doi:10.1002/hbm.23150 · Cited by 70
Basic Units of Inter-Individual Variation in Resting State Connectomes
Sripada, C., Angstadt, M., Rutherford, S. E., Kessler, D. A., Kim, Y., Yee, M. A., & Levina, E. (2019) · Scientific Reports · doi:10.1038/s41598-018-38406-5 · Cited by 80

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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks