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. 2015 Nov;18(11):1664-71.
doi: 10.1038/nn.4135. Epub 2015 Oct 12.

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

Emily S Finn  1 , Xilin Shen  2 , Dustin Scheinost  2 , Monica D Rosenberg  3 , Jessica Huang  2 , Marvin M Chun  1   3   4 , Xenophon Papademetris  2   5 , R Todd Constable  1   2   6
Affiliations

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

Emily S Finn et al. Nat Neurosci. 2015 Nov.

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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Figures

Figure 1
Figure 1. Identification analysis procedure and network definitions
a) Database and target design. Each subject had six sessions of fMRI data: a resting-state session (R1), a working memory task (WM) and a motor task (Mt) on day 1, and a resting-state session (R2), a language task (Lg) and an emotion task (Em) onday2. For identification, we used a set of connectivity matrices from one session for the database, and connectivity matrices from a second session acquired on a different day as the target set. All possible combinations of database and target sessions are indicated by the arrows connecting session pairs. b) Identification procedure. Given a query connectivity matrix from the target set, we computed the correlations between this matrix and all the connectivity matrices in the database. The predicted identity (ID*) is the one with the highest correlation coefficient. c) Node and network definitions. We used a 268-node functional atlas defined on an independent dataset of healthy control subjects using a group-wise spectral clustering algorithm. Nodes were further grouped into eight networks using the same clustering algorithm, and these networks were named according to their correspondence to other existing resting-state network definitions.
Figure 2
Figure 2. Identification accuracy across session pairs and networks
a) Identification accuracy based on all nine database and target pairs, where each row has the same database session and each column has the same target session. Each graph shows accuracy based on each individual network as well as a combination of networks 1 and 2 (the frontoparietal networks) and the whole brain (“All”). Bar shading (black or gray) indicates which session was used as the database (with the other session serving as the target). b) Identification results from the combined frontoparietal networks (top) are highlighted in color-coded matrices (bottom) to more readily compare accuracy across rest-rest, rest-task and task-task session pairs. Identification was most successful between the rest-rest pair, with a slight drop in accuracy for both rest-task and task-task pairs.
Figure 3
Figure 3. Factors affecting identification accuracy
a) Highly unique (DP, top row, red) and highly consistent (Φ, bottom row, blue) edges in individual connectivity profiles. For visualization, both sets of edges were thresholded at the 99.5 percentile. In the circle plots (left), the 268 nodes (inner circle) are organized into a lobe scheme (outer circle) roughly reflecting brain anatomy from anterior (top of circle) to posterior (bottom of circle), and split into left and right hemispheres; lines indicate edges. In the colored matrices (right), the same data are plotted as percentage of edges within and between each pair of networks; a darkly shaded cell indicates a relative over-representation of that network pair in the DP (top) or Φ (bottom) masks. PFC, prefrontal; Mot, motor; Ins, insula; Par, parietal; Tem, temporal; Occ, occipital; Lim, limbic (including cingulate cortex, amygdala and hippocampus); Cer, cerebellum; Sub, subcortical (including thalamus and striatum); Bsm, brainstem; L, left hemisphere; R, right hemisphere. b) Longer timeseries improve identification accuracy. To control for the fact that task sessions contained fewer time points than rest sessions, we recalculated rest connectivity matrices using truncated timeseries containing between 100 and 1,100 time points. Results shown are from 500 randomizations using Rest1 and Rest2 as the database and target sessions, respectively. Box represents median with 25 and 75th percentiles; whiskers represent range. c) Use of a two-matrix database improves identification rate relative to a single matrix (task or rest). Dots and error bars represent mean and range of identification rate across all possible database and target pairs, where the target matrix was always from a task session and the database consisted of a rest-task pair (n = 8 combinations), task only (n = 8) or rest only (n = 4). *p < 0.01, Mann Whitney U test.
Figure 4
Figure 4. Effect of node and network scheme on identification accuracy
a) Comparison of identification accuracy using the Shen node atlas and network definitions (left) versus the FreeSurfer (FS) node atlas and Yeo network definitions (right). Accuracy is numerically higher using the Shen scheme; the difference is exaggerated when using just the frontoparietal networks (black lines) relative to the whole brain (gray lines). b) Raw 126×126 cross-subject correlations of frontoparietal connectivity patterns from Rest 1 and Rest 2 (top; scale bar indicates r value). Row and column subject order is symmetric; thus, diagonal elements are correlation scores from matched subjects. Mean correlation coefficients for both diagonal (matched) and off-diagonal (unmatched) elements are shown in the bar graph at bottom (error bars represent ± s.d.). The overall correlation coefficients are higher using the FS+Yeo scheme, for both diagonal elements (n = 126) and off-diagonal elements (n = 15,876). **p < 10−5, two-tailed t-test. c) Cross-subject correlation matrices after z score normalization (top; scale bar indicates z score). The global difference in correlation values is eliminated since there is no significant difference in the off-diagonal z scores. However, correlations between diagonal elements are significantly higher using the Shen scheme than the FS+Yeo scheme (bottom; error bars represent ± s.d.), which helps account for the increase in identification accuracy using the Shen scheme. **p < 10−5, two-tailed t-test.
Figure 5
Figure 5. Individual connectivity profiles predict cognitive behavior
a) Results from a leave-one-subject-out cross-validation (LOOCV) analysis comparing predicted and observed fluid intelligence (gF) scores (n = 118 subjects). Scatter plot shows predictions based on the whole brain in the positive-feature network at a feature-selection threshold of p < 0.01. Each dot is one subject; gray area represents 95% confidence interval for best-fit line, used to assess predictive power of the model. b) Mean fraction of within-network edges selected in the whole-brain positive-feature (left, red) and negative-feature (right, blue) models, shown at a range of statistical thresholds for feature selection. Y-axis indicates mean fraction of edges selected across all LOOCV iterations; x-axis indicates network label (see Fig. 1c). c) Results from a LOOCV analysis in which feature selection was restricted to within-network edges in the frontoparietal networks (1 and 2), at a feature-selection threshold of p < 0.01. As in (a), each dot is one subject; gray area represents 95% confidence interval for best-fit line. d) Results from nine separate LOOCV analyses in which feature selection was restricted to within-network edges in each of the eight networks plus a combination of networks 1 and 2. Y-axis indicates correlation between predicted and observed gF scores; x-axis indicates network label. Asterisks indicate correlations significant at p < 0.05 (uncorrected). Results based on a range of feature-selection thresholds (p-values) are shown to demonstrate consistency across thresholds. Note that for some networks, no features passed the statistical thresholding step, and thus it was not possible to generate predictions; this is reflected by missing bars.
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