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Research Articles, Behavioral/Cognitive

Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness

Yuan Zhang, Aarthi Padmanabhan, James J. Gross and Vinod Menon
Journal of Neuroscience 4 September 2019, 39 (36) 7155-7172; DOI: https://doi.org/10.1523/JNEUROSCI.0220-19.2019
Yuan Zhang
1Department of Psychiatry and Behavioral Sciences,
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Aarthi Padmanabhan
1Department of Psychiatry and Behavioral Sciences,
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James J. Gross
2Department of Psychology,
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Vinod Menon
1Department of Psychiatry and Behavioral Sciences,
3Department of Neurology and Neurological Sciences, and
4Stanford Neuroscience Institute, Stanford University, Stanford, California 94305
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  • Figure 1.
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    Figure 1.

    Schematic view of participant selection procedure and main analysis steps. a, From the original 1495 participants, 157 were excluded due to incomplete data, medical problems, poor brain coverage, or not being able to perform the task. A total of 567 participants were excluded due to excess head motion, and 12 participants were excluded due to low task accuracy. A total of 759 participants 8–23 years of age survived these exclusion criteria. b, Brain ROIs involved in emotion perception were identified using meta-analysis implemented in Neurosynth (Yarkoni et al., 2011). They included the following: vmPFC, dmPFC, vlPFC, dlPFC, lOFC, IPL, SPL, sgACC, pgACC, dACC, PCC, pre-SMA, BLA, vAI, dAI, PI, hippocampus (Hipp), and FFG. Subcortical ROIs, including CMA, BNST, and NAc, were identified using anatomical atlases (see also Table 3). ROIs were overlaid on a reference brain surface using BrainNet Viewer (Xia et al., 2013). c, Network analysis steps used to investigate development of the affective circuits. ci, ROI β series were derived from each task condition (fear, anger, sad, happy, or neutral) for each individual. cii, Functional connectivity matrices were computed for each individual. ciii, Functional connectivity matrices were fed into a community detection algorithm to determine community structure of the ROIs. civ, ROI nodal degree was computed over a wide range of sparsity (10% ≤ sparsity ≤ 40% with an interval of 5%), and then an integrated metric of nodal degree was obtained by computing the area under the curve. cv, Intramodule and intermodule interactions were computed. cvi, Deviation of individual community structure from template community structure was measured as the Jaccard overlap between the two. cvii, ROIs were further categorized into hubs or nonhubs, and then the probability of an ROI to be a hub within each age year was calculated. Permutation test was used to assess hub significance. cviii, These resulting brain metrics were further fed into linear mixed-effect models to assess age, emotion, and age × emotion effects. d, Reliability analyses assessed the robustness of age-related findings and the effect of sample size. Briefly, we randomly drew N (sample size) participants from the full sample and assessed age-related changes in the subsample. This was repeated 1000 times across a wide range of sample sizes (50 ≤ N ≤ 400, increased by 50). Correlation between age effects from a subsample and that from the full sample was calculated and then averaged across subsamples for each sample size. The resulting average correlation was used to measure reliability.

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    Figure 2.

    Developmental changes in behavior and reliability. Accuracy: The ability to correctly identify high arousal-negative (threat-related) emotions (fear and anger) improved with age in both the fMRI (ai) and full behavioral samples (bi). Only reliability of significant effects was assessed. RT: Time to identify an emotion decreased with age across all five emotion categories in both the fMRI (aii) and full behavioral samples (bii). c, Reliability was measured as the probability of observing a significant correlation between accuracy and age or that between RT and age. ci, Accuracy: Reliability of age-accuracy associations increased as sample size increased for conditions that showed significant age-related changes in accuracy in the fMRI (solid line) and the full behavioral (dashed line) samples. cii, RT: Reliability of age-RT associations increased as sample size increased for all five emotion categories in the fMRI (solid line) and the full behavioral (dashed line) samples. In general, a sample size of 300–350 was needed to identify reliable (reliability > 0.7) age-accuracy association and that of 200 was needed to identify reliable age-RT association.

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    Figure 3.

    Main effects of age and emotion on modular organization and connectivity. ai, Three functional modules were identified across all participants: the FP module (red), mPFC/PCC module (green), and SPI module (yellow). aii, As shown in the group consistency matrix, the FP module (red border) includes bilateral dlPFC, vlPFC, dACC, pre-SMA, IPL, SPL, and FFG. The mPFC/PCC module (blue border) includes bilateral NAc, sgACC, pgACC, PCC, vmPFC, and dmPFC. The SPI module (yellow border) includes BLA, CMA, vAI, PI, BNST, and hippocampus (Hipp). Color bar represents the probability of two nodes being classified in the same module across participants. b, As shown in the group consistency matrices, overall modular structure is stable across all five emotion categories and age groups (children 8–12 years, adolescents 13–17 years, adults 18–23 years). c, Intra-FP connectivity and FP-SPI connectivity increased with age; intra-FP, intra-SPI, and all intermodule connectivity measures varied across emotions; age-related changes in intra-mPFC/PCC connectivity were modulated by emotion category. d, Intramodule and intermodule connectivity varied across emotions. Data are mean ± SEM.

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    Figure 4.

    Developmentally stable hubs in mPFC. a, ROI nodal degree for each emotion category. Node size is in proportion to integrated nodal degree measure. b, Probability measures averaged across all regions within each module at each age year. Overall, the mPFC/PCC module has a higher probability to be a functional hub across age than the FP and SPI modules. Data are mean ± SEM. c, Multiple regions of the mPFC/PCC module, including pgACC, vmPFC, and dmPFC of the mPFC/PCC module were identified as functional hub regions for all five emotion categories and were stable over development. Here, probability matrices are masked by the 95th percentile of empirical null distribution at each region and age year, with probability values ≤95th percentile set as 0. An age year includes all participants whose age was larger than or equal to the current age year and less than the next age year (e.g., age 8 includes all participants with age ≥8 and <9).

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    Figure 5.

    Main effects of age and emotion on regional connectivity. a, Connectivity within FP and SPI modules, between FP and SPI modules, and between mPFC/PCC and SPI modules increased with age, including left dlPFC connectivity with right dAI, right vAI connectivity with right sgACC and bilateral PI, right BNST connectivity with right IPL and SPL, and right BLA connectivity with left PI. Connectivity within mPFC/PCC and SPI modules decreased with age, including right sgACC connectivity with left vmPFC and left CMA connectivity with right CMA and left PI. Emotion effects are widespread, especially within FP module, between FP and mPFC/PCC modules, between FP and SPI modules, and between mPFC/PCC and SPI modules. b, Reliability of age-related changes in regional connectivity across all brain regions increased with sample size. A sample size of 50 is sufficient for reliability to surpass the 95th percentile of its corresponding empirical null distribution (dashed line), and a minimum sample size of 200–250 was needed to identify reliable age-related changes. c, Classification analysis showed that it was sufficient to discriminate all pairs of emotion categories using all interregional links, with classification accuracy ranging from 56.2% to 67.2% and significantly higher than chance level as assessed by permutation test.

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    Figure 6.

    Main effects of age and emotion on regional activity. ai, Brain regions showing age-related changes. Activity in cognitive control regions, including bilateral pre-SMA, dACC, dlPFC, left SPM, right IPL, and right lOFC, decreased with age. Node size was scaled to present effect size of age-related decrease. aii, Reliability of age-related changes across all brain regions. A minimum sample size of 50–100 was needed for reliability to surpass the 95th percentile of its corresponding empirical null distribution (dashed line), and a minimum sample size of 50–100 was needed to identify reliable (correlation > 0.7) age-related changes. bi, Brain regions showing emotion effects. Node size was scaled to present F value of emotion effect. bii, Activity in all brain regions, except bilateral BNST, varied across emotion category.

Tables

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    Table 1.

    Studies that examined age-related changes in amygdala activity and connectivity underlying emotion perceptiona

    StudySample sizeAge (yr)Contrast and no. of trialsTaskFindings
    Gee et al. (2013)45 (19F)4–22Fearful face versus fixation (no. of trials = 24)Participants viewed fearful faces interspersed with neutral faces in one run, and viewed happy faces interspersed with neutral faces in the other runAmygdala activity decreased with age; amygdala-vmPFC connectivity decreased with age
    Kujawa et al. (2016)61 (35F) TD7–25Emotional faces (happy, angry, or fearful) versus shapes (no. of trials per emotion category = 12)Participants were instructed to match emotion (happy, angry, or fearful) of the target face in face blocks and match shapes in shape blocksNo age-related effect in amygdala activity; amygdala-dACC connectivity decreased with age in TD but increased with age in AD for all emotions
    57 (34F) AD
    Wu et al. (2016)61 (35F)7–25Emotional faces (angry, fearful, or happy) versus shapes (no. of trials per emotion category = 12)Participants were instructed to match emotion (angry, fearful, or happy) of the target face in face blocks and match shapes in shape blocksNo age-related effect in amygdala activity; amygdala-ACC/mPFC connectivity decreased with age for all emotions
    Vink et al. (2014)60 (28F)10–24Positive versus neutral (no. of trials = 32), negative versus neutral (no. of trials = 32)Participants viewed each picture selected from the IAPS and label the valence (negative, neutral, or positive)Amygdala activity decreased with age; amygdala-mOFC connectivity increased with age
    Wolf and Herringa (2016)24 (13F) TD8–18Threat versus neutral (no. of trials = 16)Participants viewed threat and neutral images selected from the IAPS and label the valenceNo age-related effect in amygdala activity; amygdala-mOFC connectivity increased with age in TD but decreased with age in PTSD
    24 (16F) PTSD
    • ↵aIf not specified, healthy individuals were recruited in the listed studies. Functional connectivity was measured by PPI in Gee et al. (2013), Vink et al. (2014), and Wolf and Herringa (2016) and by gPPI in Kujawa et al. (2016) and Wu et al. (2016). AD, Anxiety disorder; IAPS, International Affective Picture System; PPI, psychophysiological interaction; PTSD, post-traumatic stress disorder; TD, typically developing. For MNI coordinates of amygdala and PFC regions used in these studies, see Table 4. For results of replication analysis, see Tables 5–7.

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    Table 2.

    Participant characteristics and task performance

    CharacteristicMean ± SD (n = 759)
    Age (yr)16.20 ± 3.27
    Sex (female/male)419/340
    Race (Caucasian/other)408/351
    Motion (mm)0.073 ± 0.03
    Fluid intelligencea12.62 ± 3.97
    Fear accuracy0.89 ± 0.11
    Anger accuracy0.92 ± 0.09
    Sad accuracy0.88 ± 0.11
    Neutral accuracy0.91 ± 0.09
    Happy accuracy0.99 ± 0.03
    Fear RT (s)2.65 ± 0.46
    Anger RT (s)2.48 ± 0.44
    Sad RT (s)2.68 ± 0.46
    Happy RT (s)2.09 ± 0.40
    Neutral RT (s)2.34 ± 0.44
    • ↵aFluid intelligence is measured by Penn Matrix Reasoning Test scores (i.e., total correct responses for all test trials). RT, Response time (median). For age distribution, see also Figure 1a.

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    Table 3.

    Anatomical location and MNI coordinates of emotion circuit-related nodes

    Region/abbreviationMNI coordinatesBrodmann area
    xyz
    Left BLA (BLA.L)−20−6−20
    Right BLA (BLA.R)26−4−20
    Left CMA (CMA.L)−24−99
    Right CMA (CMA.R)27−10−9
    Left sgACC (sgACC.L)−214−16BA 25
    Right sgACC (sgACC.R)620−12BA 25
    Left pgACC (pgACC.L)−2426BA 32
    Right pgACC (pgACC.R)2428BA 32
    Left dACC (dACC.L)−42230BA 32
    Right dACC (dACC.R)42230BA 32
    Left PCC (PCC.L)−2−5428BA 31
    Right PCC (PCC.R)4−5428BA 31
    Left vmPFC (vmPFC.L)−850−4BA 10
    Right vmPFC (vmPFC.R)850−4BA 10
    Left vmPFC (vmPFC.L)−250−12BA 32
    Right vmPFC (vmPFC.R)450−12BA 32
    Left vmPFC (vmPFC.L)−438−10BA 32
    Right vmPFC (vmPFC.R)438−10BA 32
    Left dmPFC (dmPFC.L)−25820BA 9
    Right dmPFC (dmPFC.R)25820BA 9
    Left pre-SMA (preSMA.L)−21846BA 6
    Right pre-SMA (preSMA.R)62438BA 32
    Left lOFC (lOFC.L)−4626−6BA 47
    Right lOFC (lOFC.R)4626−4BA 47
    Left lOFC (lOFC.L)−2818−16BA 47
    Right lOFC (lOFC.R)2622−18BA 47
    Left vlPFC (vlPFC.L)−482810BA 45
    Right vlPFC (vlPFC.R)482810BA 45
    Left dlPFC (dlPFC.L)−46830BA 9
    Right dlPFC (dlPFC.R)46830BA 9
    Left dlPFC (dlPFC.L)−412243BA 8
    Right dlPFC (dlPFC.R)412243BA 8
    Left IPL (IPL.L)−54−5044BA 40
    Right IPL (IPL.R)46−4844BA 40
    Left SPL (SPL.L)−30−5644BA 7
    Right SPL (SPL.R)38−5052BA 7
    Left FFG (FFG.L)−42−48−20BA 37
    Right FFG (FFG.R)42−48−20BA 37
    Left dAI (dAI.L)−34220BA 13
    Right dAI (dAI.R)3822−4BA 13
    Left vAI (vAI.L)−38−2−10BA 13
    Right vAI (vAI.R)446−10BA 13
    Left PI (PI.L)−38−1814BA 13
    Right PI (PI.R)38−1416BA 13
    Left BNST (BNST.L)−620
    Right BNST (BNST.R)620
    Left NAc (NAc.L)−1012−8
    Right NAc (NAc.R)1012−8
    Left hippocampus (Hipp.L)−34−18−16
    Right hippocampus (Hipp.R)32−14−16
    • View popup
    Table 4.

    MNI coordinates of amygdala and PFC regions used in previous developmental studies of face emotion perceptiona

    StudyAmygdala (x, y, z)Connectivity Seed (x, y, z) and target (x, y, z)
    Gee et al. (2013)(32, −1, −16)Right amygdala (32, −1, −16) and right vmPFC (2, 32, 8)
    Kujawa et al. (2016)(−20, −2, −20), (24, −2, −22)Left amygdala (AAL) and left dACC (−4, 30, 16)
    Right amygdala (AAL) and right dACC (2, 34, 14)
    Wu et al. (2016)Left and right amygdala from AAL atlasLeft amygdala (AAL) and left ACC/mPFC (−6, 34, 16)
    Right amygdala (AAL) and left ACC/mPFC (−4, 36, 14)
    Left amygdala (AAL) and left ACC (−8, 28, 18)
    Right amygdala (AAL) and right ACC (6, 36, 12)
    • ↵aAAL, Automated anatomical labeling. While no age-related effects were revealed in amygdala activity in Kujawa et al. (2016) and Wu et al. (2016), we created amygdala ROI based on coordinates showing significant main effect of emotion category in Kujawa et al. (2016) or used amygdala AAL atlas, which was a seed region in connectivity analysis in Wu et al. (2016), to examine age-related effects of amygdala activity in our PNC cohort.

    • View popup
    Table 5.

    Age-related changes in amygdala activity in the PNC sample using coordinates from previous studiesa

    RegionEmotionF(1,754)pEffect size
    Right amygdala in Gee et al. (2013)↓Neutral1.1210.290−0.040
    Fear0.2280.6330.018
    Anger0.1740.677−0.016
    Sad1.9650.1610.052
    Happy0.1090.741−0.012
    Left amygdala in Kujawa et al. (2016)bNeutral0.9530.3290.036
    Fear12.6170.00040.132
    Anger1.8440.1750.051
    Sad15.1400.00010.144
    Happy9.0200.0030.111
    Right amygdala in Kujawa et al. (2016)bNeutral0.5660.4520.028
    Fear2.6500.1040.061
    Anger0.1230.7260.013
    Sad2.8010.0950.063
    Happy0.7260.3940.032
    Left amygdala (AAL) in Wu et al. (2016)bNeutral1.5830.2090.047
    Fear15.7967.73e-050.147
    Anger4.5710.0330.080
    Sad20.7556.08e-060.168
    Happy10.0090.0020.117
    Right amygdala (AAL) in Wu et al. (2016)bNeutral0.2770.5990.020
    Fear5.7410.0170.089
    Anger1.9460.1630.052
    Sad5.7840.0160.089
    Happy1.5530.2130.046
    • ↵aEffect size is measured by the standardized partial coefficient of the age term. AAL, Automated anatomical labeling; ↓, developmental decreases in amygdala with mPFC was reported in the cited study. AAL indicates that the amygdala ROI was derived based on AAL atlas.

    • ↵bNo significant developmental changes were found in the cited study.

    • View popup
    Table 6.

    Age-related changes in amygdala connectivity in the PNC sample using coordinates from previous studiesa

    ConnectivityEmotionF(1,754)pEffect size
    AMY.R-vmPFC.R in Gee et al. (2013)↓Neutral0.2100.6470.017
    Fear3.4970.0620.069
    Anger1.8800.1710.051
    Sad0.9630.3270.036
    Happy0.2880.5910.020
    AMY.L-dACC.L in Kujawa et al. (2016)↓Neutral0.0080.929−0.003
    Fear8.9210.0030.109
    Anger2.5690.1090.059
    Sad0.3880.5330.023
    Happy4.5960.0320.079
    AMY.R-dACC.R in Kujawa et al. (2016)↓Neutral0.2140.6440.017
    Fear6.6020.0100.094
    Anger7.6610.0060.101
    Sad4.4200.0360.077
    Happy3.2320.0730.066
    AMY.L-mPFC.L in Wu et al. (2016)↓Neutral0.0120.914−0.004
    Fear4.1390.0420.075
    Anger1.1980.2740.040
    Sad0.0030.9580.002
    Happy1.1990.2740.040
    AMY.R-mPFC.L in Wu et al. (2016)↓Neutral0.3340.5630.021
    Fear3.7120.0540.071
    Anger7.5660.0060.101
    Sad1.3330.2490.004
    Happy2.7430.0980.061
    AMY.L-ACC.L in Wu et al. (2016)↓Neutral0.0190.8900.005
    Fear4.3510.0370.076
    Anger3.0000.0840.064
    Sad0.6020.4380.028
    Happy2.7690.0970.061
    AMY.R-ACC.R in Wu et al. (2016)↓Neutral0.0130.9090.004
    Fear4.5510.0330.078
    Anger5.9010.0150.089
    Sad4.4960.0340.078
    Happy1.2610.2620.041
    • ↵aEffect size is measured by the standardized partial coefficient of age term. ↓, Developmental decrease in amygdala-mPFC connectivity (reported in the cited study).

    • View popup
    Table 7.

    Replicability of previous developmental findings in amygdala activity and connectivity underlying face emotion perception

    StudyFindingsReplicability
    Amygdala activityAmygdala connectivity
    Gee et al. (2013)Amygdala activity decreased with age; amygdala-vmPFC connectivity decreased with ageNoNo
    Kujawa et al. (2016)No age-related effect in amygdala activity; amygdala-dACC connectivity decreased with age in TD but increased with age in AD for all emotionsPartially yesNo
    Wu et al. (2016)No age-related effect in amygdala activity; amygdala-ACC/mPFC connectivity decreased with age for all emotionsPartially yesNo
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Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness
Yuan Zhang, Aarthi Padmanabhan, James J. Gross, Vinod Menon
Journal of Neuroscience 4 September 2019, 39 (36) 7155-7172; DOI: 10.1523/JNEUROSCI.0220-19.2019

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Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness
Yuan Zhang, Aarthi Padmanabhan, James J. Gross, Vinod Menon
Journal of Neuroscience 4 September 2019, 39 (36) 7155-7172; DOI: 10.1523/JNEUROSCI.0220-19.2019
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