Supplemental Information
1. Supplemental Data
B1
Intra-subject variability
B2
Technical noise
0.20 0.90
B3
Intra-subject variability after accounting for technical noise
0.40 1.00
Inter-subject variability before accounting for Intra-subject variability
A
0.50 0.75
Inter-subject variability based on 21 right-handed subjects
C
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.
0
Seed based Networks
0.5
2.5
1.0
1.5
2.0
FPN DN MotATN Aud
Figure S2.
0.54 0.73
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
0.016
0.004
0.020
0.012
0.008
0
Sulc
al d
epth
Var
iabi
lity
FPN DNdATN Mot Vis LMBvATN
Functional Networks
0.01
0.04
0.03
0.02
0Cor
tical
Thi
ckne
ss V
aria
bilit
y
FPNDNdATNMot VisLMBvATN
A
B
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
Baldassarre, A., Lewis, C.M., Committeri, G., Snyder, A.Z., Romani, G.L., and Corbetta, M. (2012). Individual
variability in functional connectivity predicts performance of a perceptual task. Proc Natl Acad Sci U S A
109, 3516-3521.
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.
Top Related