Association of Irritability and Anxiety with the Neural Mechanisms of Implicit Face Emotion Processing in Youths with Psychopathology

Supplementary Online Content

Stoddard J, Tseng W-L, Kim P, et al. Association of irritability and anxiety with the neural mechanisms of implicit face emotion processing in youths with psychopathology. JAMA Psychiatry. Published online November 30, 2016. doi:10.1001/jamapsychiatry.2016.3282

eAppendix. Prior Research. eMethods 1. Participant Assessment. eMethods 2. Image Acquisition and Processing. eMethods 3. Estimating Neural Activation and Amygdala Functional Connectivity. eResults 1. ANX vs HV Only: A Comparison to a Prior Study. eResults 2. DMDD vs HV Only: A Comparison to a Prior Study. eResults 3. Analysis by Primary Diagnosis Instead of Anxiety and Irritability Dimensions. eResults 4. Cross Validation. eTable 1. Dimensional Measures by Primary Diagnosis. eTable 2. Excluded Participants. eTable 3. Details of Post Hoc Analyses of Medication Effects. eFigure 1. Positive Control of Activation. eFigure 2. Accuracy is Related to Irritability When Viewing the Higher Intensities of Anger. eFigure 3 Significant Connectivity Effects Not Depicted in Main Text. eFigure 4. Activation Maps of the Effect of ARI by Emotion on Activation. eReferences

This supplementary material has been provided by the authors to give readers additional information about their work.

Downloaded From: https://jamanetwork.com/ on 10/01/2021 eAppendix: Prior Research

Prior investigations involving youths with severe, chronic irritability (operationalized either as severe mood dysregulation (SMD)1-4 or DMDD5) suggest the neural basis of irritability-associated face-emotion processing impairments.2 Of note, these prior studies involved two categories of irritability-related disorders, bipolar disorder and SMD/DMDD, and were designed to investigate their shared or distinct pathophysiology.6 They generally found that chronic irritability is associated with aberrant neural responses to different face emotions in visual (e.g. inferior temporal) and affective processing areas, such as the amygdala. These studies do not consider the impact of anxiety on neural responding to face emotions, despite a rich prior literature relating anxiety to neural responses to face emotions.

In the current study, we focused on interactions between neural mechanisms mediating anxiety symptoms and those mediating irritability. Anxiety and irritability may share a common neural response to face emotions through circuits mediating threat response, approach-avoidance motivation, and/or emotional regulation.7-9 For this reason, and given strong cross-sectional associations between anxiety and irritability in children,10 it is important to examine interactions between the two traits, rather than simply examining the neural correlates of each in isolation. Indeed, high trait anxiety and anger interact to predict amygdala response to angry, but not fearful, faces in adult men.11 Anxiety and irritability also have consistent longitudinal associations i.e., childhood chronic irritability has been repeatedly associated with the development of adult anxiety in epidemiological samples.12

In this study, participants completed one of the more widely-used face-emotion-viewing paradigms. Specifically, they were asked to respond to a non-emotional feature of a facial stimulus, thereby eliciting automatic responses to its emotional features (i.e., implicit face-emotion processing).13 Data suggest that irritable youths show interpretative and attentional biases when they process faces, specifically angry faces, automatically or implicitly. For example, youths with DMDD are more likely than HV youths to label rapidly-presented ambiguous facial expressions as angry rather than happy.14 Also, an attentional bias towards angry, relative to neutral, facial expressions has been associated with severe, chronic irritability.15 Of note, such a bias represents also one of the more consistent behavioral findings in research among anxious individuals, further emphasizing the need for studies examining the mutual influence of these two dimensions.16 Thus, considerable behavioral data using face-processing tasks suggests the importance of conducting brain imaging work that extends such data in children with varying levels of anxiety and irritability. Our task included different intensities of emotional expressions, since data suggest that the intensity of the emotional stimulus may modulate neural responses in irritable youths.4,5

Downloaded From: https://jamanetwork.com/ on 10/01/2021 eMethods 1: Participant Assessment

Psychiatric diagnoses were made by master’s- or doctoral-level clinicians trained to reliability (κ>0.7) using the Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (KSADS-PL), including a module for DMDD (available on request). Diagnoses were confirmed in consensus conference chaired by a senior psychiatrist (coauthors EL, KT or DP).

Patients in the ANX group met criteria for generalized anxiety disorder (GAD), separation anxiety disorder (SAD), and/or social phobia (SoPh). The ANX sample was drawn from a treatment study. Other inclusion criteria for ANX patients were: clinically important anxiety on the Pediatric Anxiety Rating Scale17 (score≥10), impairment on the Children’s Global Assessment Scale18 (score<60), and desire for weekly treatment. Exclusion criteria for ANX patients were current Tourette’s syndrome, obsessive-compulsive disorder, major depressive disorder, post-traumatic stress disorder, current psychotropic medication use, or suicidal ideation.

Participants in all groups were excluded for lifetime history of psychosis, conduct disorder, or autism spectrum disorder. Healthy volunteers were free of any KSADS-PL diagnoses. Exclusion criteria for both patients and healthy volunteers were history of severe trauma, chronic or active medical conditions, psychoactive substance use within the past 2 months, history of head trauma, and FSIQ <70. Intelligence was measured using the Wechsler Abbreviated Scale of Intelligence.19

Measures of irritability and anxiety were collected within 60 days of scan acquisition. Irritability was measured using the Affective Reactivity Index (ARI),20 a parent- and self-rated measure validated for youth age 6-17 years. Respondents are prompted to consider their (or their child’s) feelings and behavior for the prior six months. The total scores for each child and parent were averaged, resulting in a possible score of 0-12, treated as a continuous measure.

Anxiety was measured by the Screen for Child Anxiety Related Disorders (SCARED),21 a validated, 41- item parent- and self-rated measure. In the n = 15 participants missing up to two items, missing item values were imputed with the mean of all other responses. Total scores of parent and child report averaged together ranged from 0-53.5, providing the continuous measure of anxiety symptom severity.

eMethods 2: Image Acquisition and Processing

Echoplanar images (EPI) were acquired with the following parameters: Flip angle = 50 degrees, echo time = 25 ms, repetition time = 2.3 seconds, 182 volumes per run, three runs, field of view = 240 mm, and acquisition voxel size=2.5x2.5x3mm. Total acquisition time was 21 minutes.

For anatomic registration and normalization, high resolution, T1-weighted magnetization-prepared 180 degrees radio-frequency pulses and rapid gradient-echo (MPRAGE) images were collected with flip angle = 7 degrees, minimum full echo time, inversion time = 425ms, acquisition voxel size = 1 mm isotropic.

MPRAGE images were uniformity corrected and skull stripped with FreeSurfer.22 These processed anatomic images were then used to create two alignment matrices to normalize the EPI. The first resulted from EPI affine alignment to a subject’s own processed anatomic image. The second was from anatomic image diffeomorphic alignment to AFNI’s TT_N27 Taliarach-space template. EPI images were processed by excising the first four volumes, limiting each voxel’s BOLD signal to four standard deviations from the mean trend of its time series, correcting for slice timing, normalizing by the simultaneous application of the two alignment matrices described above, smoothing using a 5 mm full width at half maximum Gaussian kernel, and scaling to a mean of 100.

Downloaded From: https://jamanetwork.com/ on 10/01/2021 eMethods 3: Estimating Neural Activation and Amygdala Functional Connectivity

Neural Activation

Processed, scaled EPI were entered into a general linear model with the following parameters: a cubic detrending polynomial, a regressor for each of six translational and rotational motion parameters, a 2 second block-GAM convolved regressor for each emotion by intensity stimulus class, neutral faces, and for trials with misidentified gender. EPI volumes were censored from this GLM regression by three criteria: 1) motion shift, defined as movement exceeding a Euclidean distance of 1 mm from the preceding volume, 2) volumes immediately preceding such a motion shift, and 3) volumes with a large fraction (>10%) of outlying time points. If more than 15% of volumes were censored, the subject was excluded (supplemental eTable 1). Parameter estimates for each condition represent % BOLD signal change and mean neural activation to the condition.

Amygdala Functional Connectivity

Generalized psychophysiological interaction (gPPI) between amygdala activity and task condition was analyzed with the following steps.

1. Processed, scaled EPI were prepared to estimate task condition by amygdala timeseries regressors orthogonal to the main effects of task and nuisance variables. To do this, processed, scaled EPI were entered into the same GLM used to estimate neural activation (above), except that censoring was not applied to the data. Censoring here would introduce temporal discontinuities that would interfere with partitioning amygdala BOLD response into task conditions. Residuals from the GLM were then used to generate task condition by amygdala timeseries interaction regressors. 2. gPPI regressors for each task condition by each amygdala timeseries were generated. Amygdala masks were defined as the maximum probable location of each amygdala in Talairach space defined by the DKD_Desai_MPM atlas23 in AFNI. Next, the mean timeseries from the residuals of the GLM in step 1 were calculated across all voxels in each amygdala mask. To finely partition the timeseries’ variance to each task condition, the timeseries were up-sampled from 0.43 to 10 Hz. We then deconvolved the up-sampled timeseries with the hemodynamic response function (HRF; 2 second block-GAM) used in the GLM from steps 1 and 3. This generates a timeseries representing the strength of the amygdala’s neural response to all stimuli. These were partitioned to each task condition by multiplying each deconvolved timeseries with a binary timeseries representing the presence (1) or absence (0) of each task condition stimulus. Next, these partitioned timeseries were reconvolved with the HRF to generate amygdala BOLD response for each condition, one for each amygdala timeseries by each task condition. There were 11 total, one for each emotion by intensity condition, the neutral face condition, and the wrong response condition. The regressors were then down-sampled to the scan acquisition TR timing to prepare for PPI regression. 3. For each amygdala, generalized PPI regression was done. On processed, scaled EPI data, the same GLM used to estimate neural activation, including censoring with the same limits, was run with the 11 PPI regressors as well as the amygdala’s mean timeseries as a main effect. The voxelwise parameter estimates for the amygdala timeseries and the 11 PPI regressors are interpreted as functional connectivity. eResults 1: ANX vs HV Only: A Comparison to a Prior Study

Here, we attempt to replicate findings from a similar fMRI paradigm in social anxiety disorder/social phobia reported by Blair et al.24 They compared neural response during implicit face-emotion processing between medication-free adolescents and adults with social phobia (SoPh) versus healthy volunteers (HV). Participants labeled the gender of neutral, angry and fearful faces. The angry and fearful faces varied in intensity (50%, 100%, and 150%). To conduct a very similar analysis to Blair et al.,24 we first generated samples of HV (n=25) and ANX (n=36) adolescents matched on age, gender, and intelligence. Participant assessment, image acquisition, and processing were as described in the current report. We averaged each subject’s neural response estimates across intensity for Angry and Fearful stimuli. Using these, we

Downloaded From: https://jamanetwork.com/ on 10/01/2021 conducted a Group (ANX, HV) by Emotion (Angry, Fearful, Neutral) ANOVA across the brain. No regions emerged in the Group by Emotion interaction at voxelwise thresholds p<.001 or p<.005, whole- brain corrected to p=.05. At p<0.005, a main effect of Group emerged in a region of the left inferior temporal lobe (fusiform, lingual gyri) extending into the declive [CoM=20.0, 73.3, -12.8; k=94; F(1,59)=31.9, p<.001; HV>ANX activation across faces v. fixation]. In region-of-interest analyses of each DKD_Desai_MPM-defined amygdala, there were no significant Group by Emotion effects. Further, no Group by Emotion effects emerged when restricting the ANX group to only those with social anxiety disorder. In sum, unlike Blair et al.24, we did not detect a group by emotion effect on activation in the amygdala or anterior cingulate cortex. eResults 2: DMDD vs HV Only: A Comparison to a Prior Study

A forerunner of the current study by Thomas et al.1 compared neural response during implicit face-emotion processing between youths with severe mood dysregulation (SMD), bipolar disorder, and healthy volunteers (HV). It used a task similar to the current study, with face-emotion pictures from the same set at the same morph intensities (50,100, and 150%) of Fearful and Angry expressions. It also included neutral expressions. To conduct a parallel analysis in the current data set, we selected processed images from the DMDD (n=37) and HV (n=22) groups, matched on age and gender. We averaged each subject’s neural response estimates across intensity for Angry and Fearful stimuli. Using these, we conducted a Group (DMDD, HV) by Emotion (Angry, Fearful, Neutral) ANOVA across the brain. No effects of or interactions with Group were detected at voxelwise thresholds p<.001 or p<.005, corrected to p=.05.

Using the current dataset, we also examined group differences (DMDD vs. HV) in regions where Thomas et al.1 identified effects of the SMD diagnosis. We defined regions of interest (ROIs) as 4 mm radius spheres centered at the peak activation in four regions where Thomas et al.1 detected differences between SMD and HV. Also following Thomas et al.1, we used each Talairach Daemon amygdala as an ROI. In these 6 ROI analyses, a Group (DMDD, HV) by Emotion (Angry, Fearful, Neutral) ANOVA did not detect any effects of or interactions with Group. eResults 3. Analysis by Primary Diagnosis Instead of Anxiety and Irritability Dimensions

Whole-brain, mixed-effects models in which ARI and SCARED were replaced by a factor Group representing primary diagnosis were run. Two results emerged in for left amygdala connectivity. First, a

Group by Emotion interaction occurred in the right lingual gyrus (F6,888 = 6.20 ; P < .001; k = 50; CoM = 4.2, -86.2, 2.6). This was driven by greater connectivity during implicit processing of angry faces in ADHD 2 2 relative to ANX (Wald X 1=10.3, P=.02) or HV (Wald X 1=9.0, P=.05). Second, a Group by Intensity

interaction occurred in the left insula (F6,888 = 7.57; P < .001; k = 45; CoM = -40.0, 2.7, 9.3). This was driven by greater connectivity during implicit processing of 150% intensity faces, across emotions, in 2 2 ADHD relative to DMDD (Wald X 1=22.6, P<.001) or HV (Wald X 1=19.5, P<.001). Caution must be taken when interpreting these results as our recruitment strategy was not designed to make the diagnostic groups mutually exclusive nor to match them on common confounds (eTable 2).

eResults 4: Cross Validation

By using cross-validation techniques based on resampling, we can test the robustness of the results to bias and how well they might predict future observations. The first validation we conducted was a form of leave one out cross-validation (LOOCV), to assess the effects of noise bias on voxel selection.25 Using LOOCV, we can define the ROIs that are independent of bias introduced by any single subject. We did this by identifying, from amongst the original ROIs, a common set of voxels with significant main effects or interactions of interest in all analyses that leave out one (LOO) subject each time. For all 115 LOO analyses in each of 768 voxels, all but one showed significant effects (p<0.05). Further, 90.2% of voxels were significant at a threshold of p<.01, 72.4% at p<.005, and 23.6% at .001. Connectivity results were

Downloaded From: https://jamanetwork.com/ on 10/01/2021 particularly robust to noise bias with 33.3% of voxels surviving at p<.001 and 89.8% at p<.005. These analyses suggest a slight effect of voxel selection bias on the exact p-values, but the results are still highly significant, with little risk of Type I errors.

The second validation that we conducted was a common k-fold (5-fold, 10 repeat) cross validation on the average activation or connectivity within an ROI for each detected association. This method estimates prediction error, or the variance, of new data about the predictions made by a mixed model based on the current data. For comparability, we normalize prediction errors as a proportion of the range of connectivity or activation. For all ROIs, the average normalized prediction error for the associations of interest is low, 0.14. This suggests that new results would vary little from the predictions made by the mixed models on the current data.

Downloaded From: https://jamanetwork.com/ on 10/01/2021 eTable 1: Dimensional Measures by Primary Diagnosis

HV DMDD ANX ADHD Comparison2 ARI .8 (1.3) 5.7 (2.7) 3.5 (2.7) 2.3 (1.7) F3,111=24.3, P<.01 M (SD) All pairs differ. SCARED 7.4 (6.3) 19.9 29.8 (9.0) 11.7 (6.3) F3,111=29.8, p<.01 M (SD) (12.5) All pairs differ, except HV vs. ADHD Age 14.2 (2.1) 13.5 (2.2) 12.2 (2.8) 13.5 (2.9) F3,111=3.0, p=.03 M (SD) HV>ANX years 2 Gender 59% 35% 59% 25% X 3=9.8, p=.02 %F ADHDDMDD 4 SES 42.7 38.6 32.3 29.4 F3,87=1.9, p=.14 M (SD) (19.5) (22.6) (13.9) (13.5) 5 Motion .061 .085 .078 .078 F2,111=1.4, p=.26 M (SD) (.034) (.044) (.050) (.048) 1. ADHD= Attention Deficit/Hyperactivity Disorder; ANX=any anxiety disorder; ARI=Affective Reactivity Index; DMDD= Disruptive Mood Dysregulation Disorder; SCARED= Screen for Child Anxiety Related Disorders; SES=socioeconomic status; 2. Comparisons by ANOVA or chi-square test, significant pairwise comparisons are noted below omnibus tests. 3. IQ was measured by the WASI. IQ data were not available for 2 participants. 4. SES was measured by the Hollingshead 2 factor index. SES data were not available for 24 participants. 5. Motion is calculated as the mean Euclidean distance of framewise volume shift after censoring.

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Exclusion Description Number of Subjects Criterion HV DMDD ANX ADHD Total Accuracy Less than 65% correct gender identification. 3 1 3 0 7 Motion Either: 3 4 5 5 17 1) More than 15% of echoplanar imaging volumes were above the censor threshold. 2) The average Euclidean distance of volume to volume shift was >0.25 mm for all volumes that remain after censoring. Aborted Participant attempted to scan, but asked to 1 1 2 0 4 Scan terminate scan before completing the task. Total 7 6 10 5 28

Downloaded From: https://jamanetwork.com/ on 10/01/2021 eTable 3: Details of Post Hoc Analyses of Medication Effects1

F test P post hoc2 Activation Analyses ARI x Emotion Interaction L Postcentral Gyrus Full sample (n=115) F2,888 = 14.0 <.001 A>F, H>F Exclude Stimulants (n=71) F2,536 = 10.2 <.001 A>F, H>F Exclude SSRIs (n=100) F2,768 = 12.2 <.001 A>F, H>F, H>A Exclude SGAs (n=97) F2,744 = 4.7 .010 H>F Exclude AEDs (n=104) F2,800 = 9.2 <.001 A>F, H>F, H>A R Fusiform Gyrus Full sample (n=115) F2,888 = 14.0 <.001 A>F, H>F Exclude Stimulants (n=71) F2,536 = 6.9 .001 A>F, H>F Exclude SSRIs (n=100) F2,768 = 14.7 <.001 A>F, H>F Exclude SGAs (n=97) F2,744 = 2.3 .098 n.s. Exclude AEDs (n=104) F2,800 = 8.3 <.001 A>F, H>F R Middle Occipital Gyrus Full sample (n=115) F2,888 = 16.9 <.001 A>F, H>F, A>H Exclude Stimulants (n=71) F2,536 = 9.9 <.001 A>F, A>H Exclude SSRIs (n=100) F2,768 = 17.1 <.001 A>F, H>F, A>H Exclude SGAs (n=97) F2,744 =6.5 .002 A>H, A>F Exclude AEDs (n=104) F2,800 = 12.2 <.001 A>F, H>F, A>H L Pulvinar Full sample (n=115) F2,888 = 19.6 <.001 A>F, H>F, H>A Exclude Stimulants (n=71) F2,536 = 12.9 <.001 A>F, H>F Exclude SSRIs (n=100) F2,768 = 17.2 <.001 A>F, H>F, H>A Exclude SGAs (n=97) F2,744 = 8.1 .003 A>F, H>F Exclude AEDs (n=104) F2,800 = 12.3 <.001 A>F, H>F, H>A R Pulvinar Full sample (n=115) F2,888 = 18.3 <.001 A>F, H>F, H>A Exclude Stimulants (n=71) F2,536 = 13.8 <.001 A>F, H>F Exclude SSRIs (n=100) F2,768 = 14.0 <.001 H>F, H>A Exclude SGAs (n=97) F2,744 = 5.2 .006 H>F Exclude AEDs (n=104) F2,800 = 10.9 <.001 H>F,H>A R Middle Cingulate Gyrus Full sample (n=115) F2,888 = 18.2 <.001 A>F, H>F, H>A Exclude Stimulants (n=71) F2,536 = 13.8 <.001 H>F, H>A Exclude SSRIs (n=100) F2,768 = 13.7 <.001 H>F, H>A Exclude SGAs (n=97) F2,744 = 5.2 .006 H>F Exclude AEDs (n=104) F2,800 = 10.9 <.001 H>F, H>A R Superior Occipital Gyrus Full sample (n=115) F2,888 = 13.5 <.001 A>F, H>F, A>H Exclude Stimulants (n=71) F2,536 = 11.9 <.001 A>F, H>F, A>H Exclude SSRIs (n=100) F2,768 = 11.8 <.001 A>F, H>F, A>H Exclude SGAs (n=97) F2,744 = 5.0 .007 A>F, A>H Exclude AEDs (n=104) F2,800 = 9.5 <.001 A>F, A>H

Connectivity Analyses ARI x SCARED x Emotion x Intensity Interaction L Amygdala to mPFC Full sample (n=115) F4,888 = 9.2 <.001 A150

Downloaded From: https://jamanetwork.com/ on 10/01/2021 Mean Exclude Stimulants (n=71) A150A, F>H, H>A Exclude Stimulants (n=71) F2,536 = 8.3 <.001 F>A, F>H Exclude SSRIs (n=100) F2,768 = 11.1 <.001 F>A, F>H, H>A Exclude SGAs (n=97) F2,744 = 14.9 <.001 F>A, F>H, H>A Exclude AEDs (n=104) F2,800 = 12.2 <.001 F>A, F>H, H>A SCARED Main Effect L Amygdala to L sgACC/OFC Full sample (n=115) F1,108 = 25.5 <.001 r113 = .37 Exclude Stimulants (n=71) F1,64 = 13.6 <.001 r69 = .40 Exclude SSRIs (n=100) F1,93 = 20.2 <.001 r98 = .39 Exclude SGAs (n=97) F1,90 = 18.9 <.001 r95 = .35 Exclude AEDs (n=104) F1,97 = 18.7 <.001 r102 = .35 SCARED x Intensity R Amygdala to R Superior Temporal Gyrus Full sample (n=115) F2,888 = 15.0 <.001 100>50; 150>50 Exclude Stimulants (n=71) F2,536 = 6.5 .002 100>50; 150>50 Exclude SSRIs (n=100) F2,768 = 9.5 <.001 100>50; 150>50 Exclude SGAs (n=97) F2,744 = 9.7 <.001 100>50; 150>50 Exclude AEDs (n=104) F2,800 = 9.6 <.001 100>50; 150>50 R Amygdala to R Superior Temporal Gyrus Full sample (n=115) F2,888 = 13.0 <.001 100>50; 150>50 Exclude Stimulants (n=71) F2,536 = 6.2 .002 100>50; 150>50 Exclude SSRIs (n=100) F2,768 = 9.7 <.001 100>50; 150>50 Exclude SGAs (n=97) F2,744 = 9.8 <.001 100>50; 150>50 Exclude AEDs (n=104) F2,800 = 10.9 <.001 100>50; 150>50 1. Iterative analyses excluding subjects by class of medication are shown. For reference, the first row in a region of interest summarizes results of the full sample as reported in the main text. Subsequent rows summarize analyses with a medication class exclusion. Any subjects taking any medication in this class as well as the four subjects for whom medication data were missing were excluded from these analyses. Medication data were collected within 30 days of scan acquisition.

2. For activation analyses, the short hand represents pairwise comparisons between face-emotions (A=angry, F=fearful, H=happy) or intensities of expression (50, 100, 150% emotional expression) that have significant differences (Holm- Bonferroni corrected p<0.05). For 4-way interaction in L amygdala connectivity, the post hocs are of fixed effects of the ARI by SCARED connectivity slope at angry, 150% intensity (A150) is compared against the grant mean of all ARI by SCARED connectivity slopes. For the main effect of SCARED on connectivity, we show the correlation between SCARED and connectivity averaged over all conditions.

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eFigure 1. Positive Control of Activation. Across subjects a two-sided t-test was performed of activation during gender labeling of the neutral face versus fixation (voxelwise Bonferroni corrected to p<.05). As expected, significant activation occurred in the left motor cortex (participants responded with a right-hand button press), fusiform cortex (participants were performing a visual discrimination task), and occipital cortex (the neutral face occupied much of the visual field).

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eFigure 2. Accuracy is Related to Irritability When Viewing the Higher Intensities of Anger. As detailed in the main text, the significant ARI by Emotion by Intensity interaction effect on accuracy was driven by a negative association between ARI and accuracy during implicit processing of 100% and 150% angry faces (lowest panels). To plot this association, we partialled out age and gender effects from accuracy across conditions and plotted the residuals against the ARI by emotion and intensity.

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eFigure 3. Significant Connectivity Effects Not Depicted in Main Text. All plots are of age, motion, and gender adjusted connectivity. Panel a shows left amygdala connectivity to the left lateral orbitofrontal cortex is modulated by SCARED and ARI score and emotion. Relative to angry expressions, connectivity to fearful expressions decreased in highly irritable, low-anxious individuals. Plots constructed as in figure 2 of the main text. Note the predicted relationship between connectivity, ARI, and SCARED averaged across intensity for Fearful. It decreases (blue quadrant) for those with low anxiety and high irritability. Connectivity does not decrease for angry faces (not shown), resulting in an increasing angry-fearful contrast in the left panel of the scatter plot. Panel b depicts the main effect of SCARED on left amygdala connectivity in the subgenual anterior cingulate. Connectivity increases with anxiety. Panel c shows connectivity to the right amygdala is modulated by SCARED and intensity in the bilateral superior temporal gyri. The scatterplot of intensity contrasts of connectivity by SCARED for the left temporal lobe. This contrasts increased with increasing SCARED, which is true also of the right temporal lobe.

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eFigure 4. Activation Maps of the Effect of ARI by Emotion on Activation. Two views for each region that showed a significant ARI by Emotion interactive effect. Across intensities, there was a positive association between irritability and activation during implicit processing of happy or angry relative to fearful faces (p’s < 0.05; main manuscript Table 2).

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