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

By Emily S. Finn, X. Shen, Dustin Scheinost, Monica D. Rosenberg, Jessica Huang, Marvin M. Chun, X. Papademetris, R. Todd Constable. Nature Neuroscience, 2015

2,614 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c5e3d1886f8b81289fd746038d85b2375c1ee883

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In Cognitive Neuroscience

Citations in the corpus

An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017, background): “…dynamics predict task-evoked activation and behaviour (Cole et al., 2014; Fox and Raichle, 2007; Fox et al., 2007), can be used to accurately identify individuals across repeated scans (Finn et al., 2015), and are under significant genetic control (Fornito et al., 2011; Glahn et al., 2010).”
Context-Aware Generative Adversarial Privacy (2017, background): “Even more recently, [26] successfully designed re-identifcation attacks on anonymized fRMI imaging datasets.”
Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, neutral): “The pipelines consisted of a combination of FreeSurfer 5.3.0 (Fischl et al., 2012), FSL 5.0.8 (Jenkinson et al., 2012; Smith et al., 2004) and in-house Matlab functions.”
Data Quality Influences Observed Links Between Functional Connectivity and Behavior (2017, methodology): “Prior work using FC data from the HCP has reported FC: behavior relationships as regards fluid intelligence and life success (Finn et al. 2015; Smith et al. 2015).”
A distributed brain network predicts general intelligence from resting-state human neuroimaging data (2018, methodology): “…of the HCP dataset, see Methods ), further revised the effect size down to about r LOFO =0.09 using methods matched as closely as possible to the original study [40]; yet, using improvements including better inter-subject alignment and multivariate modeling, we found r LOFO =0.263 (N=884) [43].”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “This dataset is publicly available as the “Unrelated 100” in the HCP database and is referred to as “Dataset 1” in the remainder of this paper.”
Comparing spatial null models for brain maps (2021, neutral): “2019), individual fingerprinting (Finn et al. 2015), modelling of brain dynamics (Proix et al.”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “, sensorimotor) regions whereas individual effects were most prominent in control, especially frontoparietal regions, consistent with past reports (Finn et al., 2015; Gordon et al., 2017b; Miranda-Dominguez et al., 2014; Mueller et al., 2013).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “…Driesen, Skudlarski, Gore, & Constable, 2006) and clinical conditions (e.g., psychiatric and neurological disorders; Fox & Greicius, 2010; M. Greicius, 2008), and can be used to predict behavioral performance (e.g., M. D. Rosenberg et al., 2016) and individual identity (e.g., Finn et al., 2015).”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, methodology): “…performed: (1) whole-brain multivariate test–retest reliability, using a method derived from the image intraclass correlation coefficient (I2C2; Shou et al. 2013), and (2) whole-brain discriminability, using 2 discrimination procedures related to the “fingerprinting” approach (Finn et al. 2015).”
Precision functional mapping of individual human brains (2017, neutral): “Similar findings have been reported by others as the basis for connectivitybased ‘‘fingerprinting’’ of individuals (Anderson et al., 2011; Finn et al., 2015; Miranda-Dominguez et al., 2014; Pannunzi et al., 2017; Xu et al., 2016).”