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.
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Supplemental Information
1. Supplemental Data
B1
Intra-subject variability
B2
Technical noise
0.20 0.90
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Intra-subject variability after accounting for technical noise
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Inter-subject variability before accounting for Intra-subject variability
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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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Seed based Networks
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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
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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)
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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)
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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
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Cox et al., 2010 21 Resting state fMRI: Connectivity originating from the right IFG and the left nucleus accumbens