Supplemental Information

1. Supplemental Data

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Intra-subject variability

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Intra-subject variability after accounting for technical noise

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Inter-subject variability based on 21 right-handed subjects

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Figure S1. (A) Inter-subject connectivity variability before regressing out measurement instability. (B1) Measurement instability as estimated by the variance across 5 scanning sessions within each subject and averaged across 23 subjects. (B2) Technical noise was estimated based on the temporal Signal-to-Noise Ratio (tSNR) maps. Highest noise level was seen in basal brain regions like the inferior frontal gyrus and the inferior temporal gyrus, where magnetic susceptibility artifacts are known to be strong. (B3) Intra-subject functional variability can be roughly estimated by regressing out the technical noise (B2) from the measurement instablility (B1). After the regression, strong intra-subject variability was seen in temporal lobe but not frontal lobe. This variability may reflect the biological variability caused by the change of brain state in combination with the noise unexplained by tSNR. (C) Inter-subject variability in resting-state functional as shown in figure 1, quantified using 21 right-handed subjects. The spatial distribution of inter-subject variability remains grossly unchanged as compared to the map that was derived in 21 right-handed and 2 left-handed subjects.

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Inter-subject variability after accounting for sulcal depth variability

Figure S3. (A) To assess the potential confound of inter-subject alignment variability on functional connectivity variability, a GLM approach was applied to regress out sulcal depth variability, which includes potential alignment errors, from the functional variability map. The overall pattern of functional connectivity variability remains stable after regression, indicating that this pattern is not dominated by functional alignment variability. (B) Sulcal depth and cortical thickness variability were

Sulcal depth variability showed a ranking pattern similar to that of functional connectivity variability, with highest variability in association cortices including the frontoparietal and attention networks, and lowest variability in the sensory-motor and the visual networks. Cortical thickness variability demonstrated a very distinct ranking pattern.

Functional Networks

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Table S1: Studies included in the meta-analysis of individual differences predicted by functional connectivity.

Study N fMRI study type Assocition with individual differences in N of foci

Bertolino et al., 2006 27 Task fMRI: Memory retrieval, connectivity originating from the hippocampal formation

Behavioral accuracy at retrieval

1

Seeley et al., 2007 14 Resting state fMRI: Connectivity within salience network (anxiety) Connectivity within executive control network (executive task performance)

Pre-scan anxiety rating Executive task performance (trail making test)

4

Ritchey et al., 2008 19 Task fMRI: Encoding of negative and neutral pictures, connectivity originating from the amygdala

Persistence of emotional memories (memory of emotional stimuli after long delay versus short delay)

6

Song et al., 2008 59 Resting state fMRI: Connectivity originating from the bilateral DLPFC

Full scale IQ 24

Buckholtz et al., 2008 123 Resting state fMRI: corticolimbic connectivity originating from the amygdala

Personality traits: harm avoidance and reward dependence

1

Cox et al., 2010 21 Resting state fMRI: Connectivity originating from the right IFG and the left nucleus accumbens

Personality trait: risk seeking/avoiding personality 2

Ploner et al., 2010 16 Resting state fMRI: Insular-PAG connectivity

Anxiety and attention to pain 2

Wang et al., 2011 59 Resting state fMRI: Regional homogeneity as a measure of local connectivity

Full scale intelligence quotient 14

Zhu et al., 2011 19 Resting state fMRI: Connectivity between occipital face area and fusiform face area

Accuracy in recognizing familiar faces 4

Cox et al., 2011 38 Resting state fMRI: Functional connectivity between social-emotional regions (insula, orbitofrontal cortex, amygdala, ACC)

Affective empathy relative to cognitive empathy 12

Harsay et al., 2011 14 Task fMRI: Reward-cued antisaccade paradigm, connectivity originating from the caudate nucleus and and the nucleus accumbens

Behavioral benefit of reward anticipation 11

Ryan et al., 2011 39 Resting state fMRI: Connectivity originating from the posterior cingulated gyrus

Personality trait: Agreeableness

10

Baldassarre et al., 2012

14 Resting state fMRI: Connectivity originating from visual cortex seeds

Visual perception, composite task score 33

Tian et al., 2012 34 Resting state fMRI: Regional homogeneity as a measure of local connectivity

Response inhibition 12

Gordon et al., 2012 77 Resting state fMRI: Connectivity between nodes of task negative and task positive networks

Executive traits (Inattention from ADHD self report scale and Impulsivity from Barratt Impulsiveness Scale)

3

 

2. Supplemental References

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Bertolino, A., Rubino, V., Sarnbataro, F., Blasi, G., Latorre, V., Fazio, L., Caforio, G., Petruzzella, V.,

Kolachana, B., Hariri, A., et al. (2006). Prefrontal-hippocampal coupling during memory processing is

modulated by COMT Val158met genotype. Biological Psychiatry 60, 1250-1258.

Buckholtz, J.W., Callicott, J.H., Kolachana, B., Hariri, A.R., Goldberg, T.E., Genderson, M., Egan, M.F., Mattay,

V.S., Weinberger, D.R., and Meyer-Lindenberg, A. (2008). Genetic variation in MAOA modulates

ventromedial prefrontal circuitry mediating individual differences in human personality. Mol Psychiatry 13,

313-324.

Cox, C.L., Gotimer, K., Roy, A.K., Castellanos, F.X., Milham, M.P., and Kelly, C. (2010). Your resting brain

CAREs about your risky behavior. PLoS ONE 5, e12296.

Cox, C.L., Uddin, L.Q., Di Martino, A., Castellanos, F.X., Milham, M.P., and Kelly, C. (2011). The balance

between feeling and knowing: affective and cognitive empathy are reflected in the brain's intrinsic functional

dynamics. Soc Cogn Affect Neurosci.

Gordon, E.M., Stollstorff, M., Devaney, J.M., Bean, S., and Vaidya, C.J. (2012). Effect of dopamine transporter

genotype on intrinsic functional connectivity depends on cognitive state. Cereb Cortex 22, 2182-2196.

Harsay, H.A., Cohen, M.X., Oosterhof, N.N., Forstmann, B.U., Mars, R.B., and Ridderinkhof, K.R. (2011).

Functional connectivity of the striatum links motivation to action control in humans. J Neurosci 31, 10701-

10711.

Ploner, M., Lee, M.C., Wiech, K., Bingel, U., and Tracey, I. (2010). Prestimulus functional connectivity

determines pain perception in humans. Proc Natl Acad Sci U S A 107, 355-360.

Ritchey, M., Dolcos, F., and Cabeza, R. (2008). Role of amygdala connectivity in the persistence of emotional

memories over time: an event-related FMRI investigation. Cereb Cortex 18, 2494-2504.

Ryan, J.P., Sheu, L.K., and Gianaros, P.J. (2011). Resting state functional connectivity within the cingulate

cortex jointly predicts agreeableness and stressor-evoked cardiovascular reactivity. Neuroimage 55, 363-

370.

Song, M., Zhou, Y., Li, J., Liu, Y., Tian, L., Yu, C., and Jiang, T. (2008). Brain spontaneous functional

connectivity and intelligence. Neuroimage 41, 1168-1176.

Tian, L., Ren, J., and Zang, Y. (2012). Regional homogeneity of resting state fMRI signals predicts Stop signal

task performance. Neuroimage 60, 539-544.

Wang, L., Song, M., Jiang, T., Zhang, Y., and Yu, C. (2011). Regional homogeneity of the resting-state brain

activity correlates with individual intelligence. Neurosci Lett 488, 275-278.

Zhu, Q., Zhang, J., Luo, Y.L., Dilks, D.D., and Liu, J. (2011). Resting-state neural activity across face-selective

cortical regions is behaviorally relevant. J Neurosci 31, 10323-10330.