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. Author manuscript; available in PMC: 2024 Feb 28.
Published in final edited form as: Psychiatry Res Neuroimaging. 2023 Apr 15;332:111645. doi: 10.1016/j.pscychresns.2023.111645

Neural correlates of irritability symptom relief in adolescents pre- and post-Trauma-Focused Cognitive Behavioral Therapy: A pilot study on reward processing

Ruiyu Yang 1,2, Yukari Takarae 2, Hailey Adney 2, Conner Swineford 2, Johanna C Walker 1,2, Philip Cheng 3, Sesen Negash 4, Jillian Lee Wiggins 1,2
PMCID: PMC10901248  NIHMSID: NIHMS1943180  PMID: 37087811

Abstract

Despite that Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) is a first-line, evidence-based treatment for youths experiencing trauma-related symptoms, treatment responses vary, and it remains unclear for whom and how this treatment works. In this context, we examined pre-treatment neural reward processing and pre- vs. post-treatment changes in neural reward processing, in relation to irritability – a transdiagnostic and dimensional feature present in multiple trauma-related symptoms, following TF-CBT. Adolescents (N = 22) with childhood trauma history completed a child-friendly monetary incentive delay task during fMRI acquisition, prior to and after the treatment, and irritability symptoms were assessed at five time points over the course of the treatment. Individual irritability slopes (i.e., irritability change rate) and intercepts (i.e., initial irritability level), generated by linear growth curve modeling, were integrated with fMRI data. Repeated ANCOVAs demonstrated that both pre-treatment neural response to reward and pre- vs. post-treatment changes in neural reward processing correlated with irritability symptom relief, such that opposite baseline neural reward processing profiles and differential changing patterns were observed in individuals showing irritability symptom relief vs. not. Together, our findings provide proof of concept that integrating brain information with clinical information has the potential to identify predictors and mechanisms of symptom relief.

Keywords: Trauma-Focused Cognitive Behavioral Therapy, Functional MRI, Irritability, Neural Reward Processing

1. Introduction

Two out of three adolescents have been exposed to at least one incidence of trauma during childhood (Magruder et al., 2017; McLaughlin et al., 2013), which elevates the risk of developing trauma-related symptoms spanning both internalizing and externalizing spectra (Kim et al., 2021; Villalta et al., 2018). Timely and developmentally appropriate intervention after trauma exposure is essential; as such, Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) serves as a first-line treatment for children and adolescents experiencing trauma-related symptoms (Cohen et al., 2011). Despite ample empirical evidence supporting TF-CBT (e.g., de Arellano et al., 2014; Jensen et al., 2014), however, around one third do not clinically improve after the treatment (Bradley et al., 2005). It remains unclear which subgroups of youths are likely to benefit vs. not from this treatment, increasing the trial-and-error burden on the treatment-seeking youths. In addition to “external” demographic or clinical features which may predict treatment effectiveness, investigating neural substrates associated with TF-CBT may shed light on the “hidden” predictors of treatment effectiveness and, moreover, elucidate mechanisms of action for TF-CBT. To date, due to the inherent difficulties coordinating treatment and pre- and post-neuroimaging, few studies have leveraged functional neuroimaging to characterize potential treatment targets and neural mechanisms of change following treatment, and even fewer following TF-CBT. These few studies have largely focused on adults diagnosed with posttraumatic stress disorder (PTSD) (e.g., Bryant, Erlinger, et al., 2021; Bryant, Williamson, et al., 2021; Butler et al., 2019) where changes in neural activation in brain regions such as the anterior cingulate cortex, amygdala, medial prefrontal cortex, and orbitofrontal cortex have been documented (Pierce & Black, 2021). One study observed changes in functional connectivity between amygdala and fronto-parietal network in adults diagnosed with PTSD and/or major depressive disorder (MDD) following TF-CBT (Shou et al., 2017). How TF-CBT works in adolescents, however, remains unclear. To our knowledge, one study to date examined the neural mechanisms of change following TF-CBT in adolescents and found a negative association between PTSD symptom improvement and functional connectivity between right amygdala and left insula, during a cognitive reappraisal task (Cisler et al., 2016).

Meanwhile, substantial neuroimaging studies examining how traumatic experiences get “under the skin” have identified trauma-related alterations in neural networks associated with reward processing (Herzberg & Gunnar, 2020; Seidemann et al., 2021), informing potential treatment targets. Similarly, the majority have focused on adults and/or populations diagnosed with PTSD (e.g., Birn et al., 2017; Keding & Herringa, 2016; Sailer et al., 2008), despite that early trauma exposure may particularly come to bear in adolescence due to normative yet seismic developmental shifts during this sensitive period (Casey et al., 2010; Merikangas et al., 2010; Somerville & Casey, 2010), and reward alterations underlie the full spectrum of trauma reactions (i.e., hypo- and hypersensitivity to reward in internalizing and externalizing symptoms, respectively) (Kim et al., 2021). Among the few neuroimaging studies conducted in adolescents, findings are mixed. For example, hypoactivation during reward anticipation has been documented (Hanson et al., 2015) in reward-related regions including ventral striatum and amygdala; meanwhile, hyperactivation toward rewarding stimuli (e.g., happy face) in regions such as dorsal anterior cingulate cortex has been observed (Keding & Herringa, 2016). The paradoxical findings observed in adolescents likely reflect the heterogeneity of trauma-related symptoms (e.g., both internalizing and externalizing symptoms) and their underlying mechanisms, amplified during this critical and rapid developmental window. In this light, the Research Domain Criteria (RDoC; Cuthbert & Insel, 2013; Insel et al., 2010) approach, focusing on specific and transdiagnostic treatment targets, may better investigate for whom and how TF-CBT works in adolescents, and thus bears translational significance in improving the precision of treatment target and lessening treatment burden (Vilar et al., 2019).

To this end, the present study, as a preliminary effort, examined potential neural predictors and neural mechanisms of change following TF-CBT in youths, with a focus on neural reward processing given its important and transdiagnostic role in trauma-related psychopathology and yet a dearth of understanding how TF-CBT may take effect in this neural modality (Herzberg & Gunnar, 2020; Seidemann et al., 2021). Clinically, the present study targeted irritability, an elevated proneness to anger featuring in both internalizing and externalizing trauma reactions, including PTSD (Brotman et al., 2017) and among the most common problems for which families of youths seek treatment (Evans et al., 2022) in adolescents. While multiple aspects of the treatment targets of TF-CBT are related via interconnecting pathways, our focus on neural reward processing and irritability was motivated by research showing strong and entwined associations between trauma, neural reward processing, and irritability (e.g., Deveney, 2019; Hodgdon et al., 2021; Kryza‐Lacombe et al., 2021). For example, Hodgdon et al. (2021) observed irritability-related alterations in amygdala connectivity with prefrontal, temporal, and parietal regions, as well as in activation of prefrontal and posterior cortices, during a reward-processing task (i.e., frustrative non-reward) in a group of youths with trauma history. Taken together, the dimensional and transdiagnostic nature of neural reward processing and irritability, as well as their close associations with trauma, make them good candidates for monitoring overall trauma-related psychological symptoms following TF-CBT in youths (Dugré & Potvin, 2022; M. Mulraney et al., 2014). The present study is among the first studies leveraging neuroimaging techniques both prior and after the treatment, despite national calls for additional research on neural mechanisms of treatment (Sanislow et al., 2010). Additionally, while most of the existing neuroimaging studies on TF-CBT were conducted in adults diagnosed with PTSD, the present study targeted treatment-seeking adolescents with trauma history (i.e., experienced at least two traumatic events) without requiring a PTSD diagnosis, given that exposure to traumatic experiences may engender sub-clinical detrimental effects, particularly in early adolescence (Busso et al., 2017). Specifically, the treatment in the present study targeted trauma-related subclinical symptoms, primarily irritability, as well as depressive and anxiety symptoms. On the basis of relevant prior neuroimaging research (e.g., Bryant, Erlinger, et al., 2021; Pierce & Black, 2021; Shou et al., 2017), we hypothesized that: 1) baseline (i.e., pre-treatment) neural activation and functional connectivity in reward-related brain regions (e.g., amygdala, ventral striatum, prefrontal and temporal cortices) during a reward processing fMRI task will correlate with the degree of irritability symptom improvement following TF-CBT, suggesting a predictive role of baseline neural reward processing profiles, and 2) pre- vs. post-treatment changes in neural activation and functional connectivity in reward/emotion-related brain regions will correlate with changes in irritability symptoms over the same time period, suggesting an active pathway of change.

2. Methods

2.1. Participants

Thirty-one treatment-seeking participants (M = 14.29, in years) with childhood trauma exposure were recruited from a local middle/high school, and the demographic characteristic of the sample reflected that of the surrounding area (Table 1). To expand the applicability of research in this field to marginalized groups, we focused on a racial/ethnic minority (primarily Latinx) and low socioeconomic status sample; to maximize sensitivity to trauma reactions that may not rise to the level of PTSD diagnosis, we recruited for trauma exposure without requiring a PTSD diagnosis. Exclusion criteria included magnetic resonance imaging contraindications (e.g., metal implants, orthodontic braces, claustrophobia, weight over 300 lbs.), major medical problems with clear impact on the central nervous system, participant being not able to understand procedures sufficiently to provide assent (based on the assessment from a qualified research team member). Of the 31 participants, n = 22 (70.97%) completed more than 80 percent of the TF-CBT sessions and n = 9 (29.03%) dropped out (see Supplement for details). Participants who completed the treatment vs. who dropped out were comparable in demographic and baseline clinical characteristics, except that those who dropped out reported more childhood trauma (Table S1).

Table 1.

Sample Demographic Characteristics (N = 22)

Mean SD Range
Age 14.29 1.85 11.92–18.68
Childhood Trauma* 36.33 8.55 27–62
Irritability symptoms* 3.82 3.29 0–10
Anxiety symptoms* 24.18 16.72 4–56
Depression symptoms* 9.62 11.57 0–40
Number of Completed Therapy Sessions 11.73 .70 10–12
Household Income/Month ($) 4,207.31 3,853.10 1,700–16,000
N Percentage
Gender - Female 13 59.1
Race/Ethnicity
African American/Black 1 4.5
Asian/Pacific Islander 2 9.1
Hispanic/Latinx 17 77.3
Biracial 2 9.1
Maternal Education**
Less than 7th Grade 5 22.7%
Jr. High School (9th Grade) 2 9.1%
Some High School (10th or 11th Grade) 2 9.1%
High School Graduate 1 4.5%
Some College (at least 1 year) 2 9.1%
Standard College Degree 2 9.1%
Graduate Professional Training 0 N/A
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*

Collected prior to the treatment. See Supplement for details.

**

Based on available responses from participants’ parent/caregiver (n = 8 missing).

2.2. Study Procedure Overview

All participants completed an initial assessment at baseline, including an fMRI scan, then underwent the weekly 12-session manualized TF-CBT; finally, they completed a post-treatment fMRI scan. Treatment sessions were delivered by Marriage and Family Therapist trainees, with manualized TF-CBT trainings and clinical supervisions. Irritability symptom assessments were obtained at baseline, post-treatment, and every four weeks during the treatment, totaling five assessment points. Study procedures and consent forms were approved by the University of California San Diego Institutional Review Board and accepted by joint agreement by the San Diego State University Institutional Review Board. Informed consent from participants more than 18 years of age, assent from minors and consent of their parents were obtained. Participants received therapy at no cost and received monetary compensation for baseline and post-treatment fMRI scans.

2.3. Measures

2.3.1. Irritability

Irritability was assessed using the self-report scores on the Affective Reactivity Index [ARI] (Stringaris et al., 2012). The ARI is a 7-item scale assessing feelings and behaviors specific to irritability, with higher scores indicating more severe irritability. The ARI has good psychometric properties (Cronbach’s Alpha = .88 - .90) (M. A. Mulraney et al., 2014; Stringaris et al., 2012) and has been used with adolescents across multiple studies (e.g., Dougherty et al., 2018, 2021; Evans et al., 2022). See Supplement for a detailed description of measure administration.

2.3.2. fMRI Data Acquisition

Subjects were scanned using a 3T Siemens Magnetom Prisma with a 30-channel head coil using multiband procedures to increase spatial and temporal resolution, and thus more sensitive detection of neuropredictors and neural mechanisms (Hagler et al., 2019). Subjects were scanned twice, before the start of the treatment and after completing the treatment. T2 blood oxygen level dependent (BOLD) images were acquired using a 3D multiband EPI pulse sequence across 3 runs. Each run consisted of 60 interleaved sagittal slices approximately parallel to the AC-PC line, with whole-brain coverage (voxel size = 2.4×2.4×2.4mm, 358 image volumes per run, matrix size = 104×104×60, acceleration factor = 6, TR = 800ms, TE = 30.8ms, flip angle = 52°, FOV = 216mm). Anatomical images with prospective motion correction (T2-weighted MPRAGE PROMO) were obtained for anatomical localization and spatial normalization (429 sagittal slices, flip angle = 9°, matrix size = 256×256×176, FOV = 256mm, voxel size = 1×1×1mm).

During fMRI acquisition, brain activity associated with reward anticipation and feedback was assessed using the piñata task, a previously validated and widely used child-friendly monetary incentive delay task (Figure S1) (Helfinstein et al., 2013; Knutson et al., 2000). See Supplement for a detailed task description.

2.4. Statistical Analysis

2.4.1. Behavioral Analysis

Pre- vs. post-treatment paired samples t-test on irritability symptoms was conducted to examine whether our sample, on average, demonstrated significant irritability improvement after TF-CBT. Additionally, we fit a linear growth curve model in MPlus based on the five irritability assessment scores to generate more accurate estimates of change over time, compared with more simplistic alternative approaches like pre/post difference scores. An intercept (i.e., pre-treatment irritability) and slope (i.e., rate of change in irritability over the course of the treatment) was generated for each participant and integrated into the second level analysis (see Second Level Analysis) with neural activation and functional connectivity. See Supplement for a detailed description of how growth curve models were fitted and a distribution of irritability intercepts and slopes (Table S2, Figure S2).

2.4.2. fMRI Data Preprocessing and First Level Analysis

See Supplement for details on fMRI data preprocessing.

Individual-level general linear models were run to generate estimates of brain activation. Reward condition (reward vs. no reward) was convolved using AFNI’s 3dMVM program (Chen et al., 2014) with “dmBLOCK” basis function over the variable duration and performance (hit vs. miss) was convolved with the “BLOCK” function. Analyses generated beta coefficients at each voxel for reward, no reward, reward/hit, reward/miss, no reward/hit, and no reward/miss trials. Additionally, generalized psychophysiological interaction analysis (McLaren et al., 2012) was used to calculate functional connectivity during the anticipation and feedback periods. Bilateral amygdalae and ventral striata were used as seed regions in gPPI analyses as previous research has shown their involvement in reward processing, irritability, and trauma (Herzberg & Gunnar, 2020; Kryza-Lacombe et al., 2021). Seed regions were identified using the Talairach atlas in AFNI (left amygdala = 1288 mm3; right amygdala = 1280 mm3; left ventral striatum = 136 mm3; right ventral striatum = 168 mm3). These analyses resulted in voxel-wise images representing connectivity between the seed region and the rest of the brain, for each condition. For both activation and connectivity models, nuisance regressors included head motion (x, y, z, roll, pitch, yaw directions) and third-degree polynomials to model low-frequency drift.

2.4.3. Second Level Analysis

First, to examine the associations between pre-treatment neural reward processing and irritability symptom changes following TF-CBT, while controlling for pre-treatment irritability., whole-brain, repeated measures ANCOVA models via AFNI’s 3dMVM (Chen et al., 2014) program were performed for activation and functional connectivity during reward anticipation and feedback. During reward anticipation, 3dMVM program with reward condition (reward vs. no reward) as the within-subject factor, and irritability slope and intercept as the quantitative between-subjects variables were modelled. The contrasts of primary interest for reward anticipation were the Slope simple effect and Slope X Reward Condition. During reward feedback, performance (hit vs. miss) was added as a within-subject factor and the contrast of primary interest was Slope X Reward Condition X Performance. Supplementally, we examined the Slope simple effect, Slope X Reward Condition, and Slope X Performance during reward feedback (Table S5a, Table S6a).

Second, to investigate the associations between pre- vs. post-treatment neural reward processing changes and irritability symptom changes following TF-CBT, while controlling for pre-treatment irritability, 3dMVM program with reward condition (reward vs. no reward) and time (pre vs. post) as the within-subject factors, and irritability slope and intercept as the quantitative between-subjects variables were modelled. The contrasts of primary interest for reward anticipation were the Slope simple effect, Slope X Time, and Slope X Reward Condition X Time. During reward feedback, performance (hit vs. miss) was added as a within-subject factor and the contrasts of primary interest were Slope X Performance X Time and Slope X Reward Condition X Performance X Time. Supplementally, we examined the Slope simple effect, Slope X Time, and Slope X Reward Condition X Time during reward feedback (Table S5b, Table S6b). See Supplement for a more detailed account of the second level analysis.

Cluster-wise FWE-correction was used to control for false positives. A whole brain corrected threshold of p < .05 and a height (voxelwise) threshold of p < .005 were used for all models. Cluster threshold was estimated using 3dClustSim with mixed-model spatial autocorrelation function (-acf) and the NN1 bi-sided option, allowing for separate clusters of positive and negative voxels. 3dClustSim applied a group mask consisting of brain regions where 90% of participants had valid data. A whole brain corrected threshold of p < .05 and a height (voxelwise) threshold of p < .005 was used for all models.

Additionally, post-hoc analyses controlling for depressive symptoms, anxiety symptoms, age, pubertal status, gender, race/ethnicity, number of treatment sessions completed, childhood trauma experiences, maternal education, and monthly household income were conducted (see Supplement for details).

3. Results

3.1. Behavioral Results

On average, irritability symptoms significantly decreased from pre- to post-treatment (t = 2.11, p = .047, Cohen’s d = .421). Despite the overall decrease in clinical symptoms, there was significant variability in individual changes in irritability (Table S2, Figure S2). Person-centered models captured this variation by generating individual slopes and intercepts for use in subsequent second level analysis (Figure S3). The linear growth curve model fit well statistically (χ2 [10, N = 22] = 12.97, p = .2255) and relatively well descriptively (CFI = .963, RMSEA = .116, SRMR = .096) (see Supplement for details).

3.2. fMRI Results

First, we examined associations between reward-related neural activity at baseline and irritability symptom changes over the treatment, controlling for initial levels of irritability. Results were presented in Table 2 and illustrated in Figure 1. Second, we examined the associations between pre- vs. post-treatment reward-related neural activity changes and irritability symptom changes over the course of the treatment, controlling for initial levels of irritability. Results were presented in Table 3 and illustrated in Figure 2. For illustrative purposes, we plotted the predicted neural activation and functional connectivity using one standard deviation below the average irritability slope (i.e., irritability decreasing) one standard deviation above the average irritability slope (i.e., irritability increasing), and zero irritability slope (i.e., irritability staying the same), within our data.

Table 2.

Pre-treatment Neural Reward Processing Patterns Correlated with Irritability Symptom Improvement

Contrast of Primary Interest Brain Regions Direction of Effects- For individuals showing irritability symptom relief, compared with individuals showing symptom maintenance or increase
Neural Activation
Reward Anticipation
Slope X Reward Condition Right ventromedial prefrontal cortex, bilateral precuneus (Figure 1, A.1) Lower activation in no-reward trials
Right inferior parietal lobule (A.1) Lower activation in reward trials
Higher activation in no-reward trials
Reward Feedback
Slope X Performance
 Left dorsomedial and ventral prefrontal cortices, left insula, left cingulate gyrus; right anterior cingulate, right medial/superior frontal gyri; bilateral caudate (A.2) Lower activation in miss (i.e., missed the target) trials
Functional Connectivity
Reward Anticipation
Slope X Reward Condition Left amygdala with right temporoparietal junction, right dorsal prefrontal cortex, bilateral precuneus (B.1) Lower connectivity in reward trials
Higher connectivity in no-reward trials
Right amygdala with right middle/superior frontal gyri (C.1) Higher connectivity in reward trials
Lower connectivity in no-reward trials
Reward Feedback
Slope X Reward Condition X Performance Left amygdala with right temporoparietal junction, right inferior and superior parietal lobule, right postcentral gyrus (B.2) Lower connectivity in miss trials when no reward was expected
Left Ventral Striatum with left temporoparietal junction, left inferior parietal lobule, left supramarginal gyrus, right cuneus, right superior parietal lobule (D.1) Lower connectivity in miss trials when no reward was expected
Right ventral striatum with bilateral precuneus, left dorsal prefrontal cortex, left superior parietal lobule/postcentral gyrus, left middle frontal/precentral gyri, left cingulate gyrus (E.1) Lower connectivity in miss trials when no reward was expected
Slope X Performance Right amygdala with left inferior parietal lobule (C.2) Lower connectivity in hit (i.e., hit the target) trials
Higher connectivity in miss trials
Right ventral striatum with right dorsolateral prefrontal cortex (E.1) Lower connectivity in miss trials
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*

Reward anticipation and reward feedback, compared with task baseline.

Figure 1. Associations between pre-treatment neural reward processing and irritability changes.

Figure 1.

Figure 1.

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Brain images depict selected significant clusters with threshold set at whole-brain corrected p = < .05. For illustrative purposes, we plotted the predicted neural activation and functional connectivity using one standard deviation below the average irritability slope (“Irr Decrease”, in green), one standard deviation above the average irritability slope (“Irr Increase”, in red), and zero irritability slope (“Irr Same”, in gray), within our data. See Supplement for a distribution of irritability slope and intercept scores. Only one cluster is plotted as an example when a contrast contains multiple regions with similar patterns. See Table S5a (Supplement) for a listing of all significant clusters.

Table 3.

Pre- vs. Post-treatment Changes in Neural Reward Processing Patterns Correlated with Irritability Symptom Improvement

Contrast of Interest Brain Regions Direction of Effects - For individuals showing irritability symptom relief, compared with individuals showing symptom maintenance or increase
Neural Activation
Reward Feedback
Slope X Performance X Time Bilateral dorsomedial prefrontal cortices (Figure 2, A.1) Decreased activation in hit trials
Increased activation in miss trials
Functional Connectivity
Reward Anticipation
Slope X Reward Condition X Time Left amygdala with bilateral medial frontal/paracentral lobule, right temporoparietal junction, right precuneus, right inferior parietal lobule (B.1) Increased connectivity in reward trials
Decreased connectivity in no-reward trials
Right ventral striatum with left insula, left medial prefrontal cortex, and right ventromedial prefrontal cortex, right anterior cingulate gyrus (E.1) Decreased connectivity in reward trials
Increased connectivity in no-reward trials
Slope X Time Right amygdala with right temporoparietal junction (C.1) Decreased connectivity from pre- to post-treatment
Right ventral striatum with bilateral precuneus, right posterior cingulate (E.1) Decreased connectivity from pre- to post-treatment
Reward Feedback
Slope X Reward Condition X Performance X Time Left amygdala with right dorsolateral prefrontal cortex (B.2) Increased connectivity in miss trials when no reward was expected
Right amygdala with left temporoparietal junction and left medial prefrontal cortex (C.2) Increased connectivity in miss trials when a reward was expected
Decreased connectivity in miss trials when no reward was expected
Left ventral striatum with left inferior parietal lobule (D.1) Decreased connectivity in miss trials when a reward was expected
Increased connectivity in miss trials when no reward was expected
Slope X Performance X Time Left amygdala with right ventromedial prefrontal cortex (B.2) Increased connectivity in miss trials
Right ventral striatum with left precentral gyrus (E.2) Decreased connectivity in miss trials
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*

Reward anticipation and reward feedback, compared with task baseline.

Figure 2. Associations between pre- vs. post-treatment changes in neural reward processing and changes in irritability.

Figure 2.

Figure 2.

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Only one cluster is plotted as an example when a contrast contains multiple regions with similar patterns. See Table S5b (Supplement) for a listing of all significant clusters.

Overall, we found that youths who showed irritability symptom relief vs. those who did not, demonstrated consistent opposite patterns in their response to reward at baseline, as well as in how their neural response to reward changed from pre- to post-treatment following TF-CBT. Of note, the column “Direction of Effects” in both Table 2 and Table 3 described neural activation and functional connectivity patterns associated with irritability symptom relief (i.e., one standard deviation below the average irritability slope), when compared with irritability symptom maintenance and worsening (i.e., zero irritability slope and one standard deviation above the average irritability slope, respectively). See Table S5a and S5b (Supplement) for statistics associated with each contrast and cluster.

4. Discussion

This preliminary study is among the first studies leveraging functional neuroimaging both prior to and after TF-CBT in youths. Specifically, we examined neural reward processing as a potential predictor and mechanism of change in relation to irritability symptom relief following TF-CBT, providing proof of concept that integrating behavioral and brain information may inform for whom and how TF-CBT works (Cuthbert & Insel, 2013; Insel et al., 2010). First of all, the imperative to “first, do no harm” is crucial given that a subset of youths may not respond to this first-line treatment. Indeed, while, on average, participants in the present study showed significant improvement in irritability symptoms, in alignment with existing empirical evidence for TF-CBT (de Arellano et al., 2014), significant individual variability in irritability symptom change was detected such that some youths improved but others did not respond to the treatment or even worsened in irritability, as has been documented in other studies (Diehle et al., 2015; Schermuly-Haupt et al., 2018). In addition to irritability as an “external” or clinical feature that demonstrated variability across individuals, we found consistent differences in neural reward processing patterns across individuals, specifically between those who showed positive response to the treatment (i.e., with decreased irritability symptom level) vs. who did not (i.e., with increased and/or same irritability symptom levels), in relation with irritability symptom changes following TF-CBT. This finding, in general, is in alignment with previous research documenting trauma-related alterations in neural response to reward (Herzberg & Gunnar, 2020; Sailer et al., 2008; Seidemann et al., 2021) and the entwined associations between trauma, irritability, and neural reward processing (Badour & Feldner, 2013; Deveney, 2019; Dougherty et al., 2018). Additionally, given that the vast majority of the existing empirical evidence has focused on adults meeting diagnostic criteria for PTSD, the present study, by investigating neural reward processing in relation to irritability – a transdiagnostic feature present in multiple forms of trauma-related symptoms – in adolescents, may shed light on the “hidden” predictors and mechanisms of action for TF-CBT in this critical developmental window.

Second, across task conditions and time periods and for both neural activation and functional connectivity analyses, consistent opposite patterns of reward processing between youths who showed irritability symptom improvement vs. not ware observed in multiple brain regions associated with reward- and emotion-processing (e.g., ventromedial and dorsolateral prefrontal cortex, ventral striatum, insula, precuneus, temporoparietal junction). These brain regions emerged in previous research probing how early life stress (e.g., childhood trauma experiences) was associated with alterations in neural development (Akiki et al., 2017; Herringa, 2017; McLaughlin et al., 2019), and examining the neural correlates of pediatric irritability symptoms (Brotman et al., 2017; Deveney, 2019; Perlman et al., 2015). Yet, exactly how these differences between the youths who improved and those who did not manifested itself differed on the basis of task conditions and seemed to convey paradoxical messages. For example, lower levels of amygdala/ventral striatum connectivity with fronto-parietal and temporal networks during no-reward and miss trials (i.e., missing the target when no reward was expected) at baseline were consistently associated with irritability symptom relief, suggesting that youths who showed similar functional connectivity patterns may be more likely to improve following the treatment; yet it was predicted that both increased and decreased (i.e., from pre- to post-treatment) levels of connectivity between amygdala/ventral striatum and fronto-parietal and temporal regions correlated with irritability symptom relief, potentially reflecting the heterogenous pathways of change following TF-CBT, which the present study was not sufficiently powered to investigate. Indeed, such heterogeneity aligned with the few neuroimaging studies conducted in adolescents that looked into potential mechanisms of both trauma-induced psychopathology and TF-CBT (e.g., Bryant, Erlinger, et al., 2021; Bryant, Williamson, et al., 2021; Keding & Herringa, 2016), which documented complex and heterogeneous relationships depending on the paradigms used, task conditions, and modalities of interest.

Despite the intricate relationship between neural reward processing patterns and irritability symptom relief, some consistent patterns emerged. Probing the relationship between pre-treatment neural reward processing and irritability symptom change, we observed that, when the target was missed and no reward was expected, lower connectivity between amygdala/ventral striatum and temporal, parietal, and prefrontal cortices was associated with irritability symptom relief. Looking at pre- vs. post-treatment differences, we found that an overall decrease in connectivity between amygdala/ventral striatum and fronto-parietal and temporal regions was associated with irritability symptom relief. Taken together, our findings suggest that more automatic processing of emotions and reward, either as a pre-treatment feature, or “learned” over the course of TF-CBT, may predict and signal irritability symptom relief and potentially better treatment outcomes (Berboth & Morawetz, 2021; Dixon et al., 2017; Herzberg & Gunnar, 2020). Clinically, TF-CBT places an emphasis on implementing developmentally appropriate affective and cognitive coping skills to help the client better identify and regulate their emotions, using different strategies (e.g., identifying the Cognitive Triangle of thoughts, feelings, and behaviors, normalizing conflicting feelings – especially trauma-related negative ones). In this light, the attenuated coupling effect between emotion-regulation and emotion/reward-processing regions may reflect a more spontaneous and faster way of coping with negative emotions, serving as one of the action mechanisms for TF-CBT. Given the preliminary nature of the present study, however, future studies with larger sample sizes are needed to elucidate and robustly test such pathways. Nevertheless, changes observed in the present study following TF-CBT suggested one of the roles of timely and effective treatments in possibly rectifying aberrant neural networks linked with emotion and reward processing, which may have been disturbed by early adverse experiences (e.g., traumatic events).

Third, our findings suggested that both the associations between pre-treatment neural response to reward and irritability symptom relief, and the associations between pre- vs. post-treatment neural reward processing changes and irritability symptom relief, were overwhelmingly driven by the no reward and/or miss trials. For instance, individuals who showed irritability symptom relief demonstrated lower levels of activation in ventromedial prefrontal cortex and bilateral cuneus prior to the treatment in no reward trials, but such difference was largely attenuated in reward trials; Individuals showing irritability symptom relief also showed lower levels of activation in regions such as the left dorsomedial and ventral prefrontal cortices, left insula, right anterior cingulate gyrus prior to the treatment in miss trials, whereas the difference was close to null in hit trials (Tables 2, 3). The emphasis on no reward and miss trials was observed in functional connectivity as well, with individuals who improved in irritability showing more significant changes in no reward trials vs. reward trials. Taken together, these findings may suggest that differences in neural activity profiles between TF-CBT responders vs. non-responders can lie primarily in how they respond when no reward is expected and/or when a target is not successfully hit, which has been largely ignored in current neural reward processing research, as the primary focus has been cast on neural activity to rewarding stimuli such as happy faces and monetary reward (e.g., Birn et al., 2017; Hanson et al., 2015). Future treatment studies may focus more on emotion regulation in “low-stakes” scenarios where rewards are not prominent, in addition to “high-stakes” scenarios when rewards are on the line.

The present study is limited in several ways. First, as part of this preliminary effort to characterize inter-individual variation in response to active treatment, all participants were delivered active TF-CBT, and we opted not to have a control group. Although the effectiveness of TF-CBT is well-established, the lack of a control group made it difficult to determine whether the changes observed at pre- vs. post-treatment were uniquely attributable to the treatment. Next, the present study has a relatively small sample size, especially given the relatively high dropout rates commonly seen in treatment research, thus the statistical power of the models tested in the present study was undermined and replications with larger sample sizes are needed. Of note, the nine participants who dropped out from the study reported more childhood trauma experiences vs. those who completed more than 80 percent of the treatment sessions. Our study may have “missed” the youths most in need of treatment and future studies may wish to further pursue issues pertaining to attrition by looking into the reasons for drop-out, which may inform better treatment delivery (Wamser-Nanney & Steinzor, 2017). Additionally, the present study did not have long-term follow up with the participants after the treatment was completed and was thus limited in demonstrating the longitudinal clinical significance of TF-CBT. It is possible that individuals who did not improve or even got worse during the treatment did not show symptom relief due to the adverse effect associated with the exposure element of TF-CBT (i.e., in vivo or imaginal exposure to traumatic events), and could have shown symptom relief at a later time that we failed to capture. Future studies may perform longer term follow up with participants (e.g., 6 months, 12 months after the treatment) to investigate if TF-CBT has lasting vs. transient effects and to capture potentially delayed positive (or negative) effects.

Taken together, the present study, as a preliminary effort, is among the first studies leveraging neuroimaging techniques to examine pre- vs. post-TF-CBT neural activity changes in adolescents with trauma-related symptoms, when the majority of existing empirical evidence focused on adults diagnosed with PTSD and rarely looked into treatment-related neural changes. Specifically, we demonstrated that youths showing irritability symptom relief vs. not after TF-CBT exhibited consistent differences (i.e., opposite patterns) in their neural response to reward at baseline, as well as in how such response changed from pre- to post-treatment, providing preliminary evidence that integrating clinical and neural information has the potential to better predict treatment responses and elucidate treatment mechanisms.

Supplementary Material

Supplement

Acknowledgements

This work was supported by the NARSAD Young Investigator Grant (26802) and the Clinical and Translational Research Institute Pilot Grant (NIH UL1TR001442) to JLW.

We are grateful to Drs. Feion and Miguel Villodas for assistance in recruitment for a portion of the sample, as well as the Gompers Preparatory Academy staff, including Director Vincent Riveroll, Assistant Director Lisa Maples, Ms. Victoria Canto, and the rest of the counseling department. We gratefully acknowledge the team of San Diego State University marriage and family therapy trainees who implemented the manualized treatment. We want to express our sincere gratefulness to all the families and youths who participated in the study. We thank the research assistants of the Translational Emotion Neuroscience and Development (TEND) Lab for help in data collection.

Abbreviations

TF-CBT

Trauma-Focused Cognitive Behavioral Therapy

fMRI

functional magnetic resonance imaging

ANCOVA

Analysis of Covariance

Footnotes

Conflict of interest

None of the authors have a conflict of interest to declare.

Data availability

Due to the high sensitivity of the data, data with potentially identifying information removed are available from the corresponding author upon written request with the permission of the senior author.

References

  1. Akiki TJ, Averill CL, & Abdallah CG (2017). A Network-Based neurobiological model of PTSD: Evidence from structural and functional neuroimaging studies. Current Psychiatry Reports, 19(11), 81. 10.1007/s11920-017-0840-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Badour CL, & Feldner MT (2013). Trauma-related reactivity and regulation of emotion: Associations with posttraumatic stress symptoms. Journal of Behavior Therapy and Experimental Psychiatry, 44(1), 69–76. 10.1016/j.jbtep.2012.07.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Berboth S, & Morawetz C (2021). Amygdala-prefrontal connectivity during emotion regulation: A meta-analysis of psychophysiological interactions. Neuropsychologia, 153, 107767. 10.1016/j.neuropsychologia.2021.107767 [DOI] [PubMed] [Google Scholar]
  4. Birn RM, Roeber BJ, & Pollak SD (2017). Early childhood stress exposure, reward pathways, and adult decision making. Proceedings of the National Academy of Sciences, 114(51), 13549–13554. 10.1073/pnas.1708791114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bradley R, Greene J, Russ E, Dutra L, & Westen D (2005). A Multidimensional Meta-Analysis of Psychotherapy for PTSD. American Journal of Psychiatry, 162(2), 214–227. 10.1176/appi.ajp.162.2.214 [DOI] [PubMed] [Google Scholar]
  6. Brotman MA, Kircanski K, Stringaris A, Pine DS, & Leibenluft E (2017). Irritability in youths: A translational model. American Journal of Psychiatry, 174(6), 520–532. 10.1176/appi.ajp.2016.16070839 [DOI] [PubMed] [Google Scholar]
  7. Bryant RA, Erlinger M, Felmingham K, Klimova A, Williams LM, Malhi G, Forbes D, & Korgaonkar MS (2021). Reappraisal-related neural predictors of treatment response to cognitive behavior therapy for post-traumatic stress disorder. Psychological Medicine, 51(14), 2454–2464. 10.1017/S0033291720001129 [DOI] [PubMed] [Google Scholar]
  8. Bryant RA, Williamson T, Erlinger M, Felmingham KL, Malhi G, Hinton M, Williams L, & Korgaonkar MS (2021). Neural activity during response inhibition associated with improvement of dysphoric symptoms of PTSD after trauma-focused psychotherapy—An EEG-fMRI study. Translational Psychiatry, 11(1), 218. 10.1038/s41398-021-01340-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Busso DS, McLaughlin KA, & Sheridan MA (2017). Dimensions of Adversity, Physiological Reactivity, and Externalizing Psychopathology in Adolescence: Deprivation and Threat. Psychosomatic Medicine, 79(2), 162–171. 10.1097/PSY.0000000000000369 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Butler O, Willmund G, Gleich T, Zimmermann P, Lindenberger U, Gallinat J, & Kühn S (2019). Cognitive Reappraisal and Expressive Suppression of Negative Emotion in Combat-Related Posttraumatic Stress Disorder: A Functional MRI Study. Cognitive Therapy and Research, 43(1), 236–246. 10.1007/s10608-018-9905-x [DOI] [Google Scholar]
  11. Casey BJ, Jones RM, Levita L, Libby V, Pattwell SS, Ruberry EJ, Soliman F, & Somerville LH (2010). The storm and stress of adolescence: Insights from human imaging and mouse genetics. Developmental Psychobiology, n/a-n/a. 10.1002/dev.20447 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chen G, Adleman NE, Saad ZS, Leibenluft E, & Cox RW (2014). Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model. NeuroImage, 99, 571–588. 10.1016/j.neuroimage.2014.06.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cisler JM, Sigel BA, Steele JS, Smitherman S, Vanderzee K, Pemberton J, Kramer TL, & Kilts CD (2016). Changes in functional connectivity of the amygdala during cognitive reappraisal predict symptom reduction during trauma-focused cognitive–behavioral therapy among adolescent girls with post-traumatic stress disorder. Psychological Medicine, 46(14), 3013–3023. 10.1017/S0033291716001847 [DOI] [PubMed] [Google Scholar]
  14. Cohen JA, Mannarino AP, & Iyengar S (2011). Community Treatment of Posttraumatic Stress Disorder for children exposed to intimate partner violence: A randomized controlled trial. Archives of Pediatrics & Adolescent Medicine, 165(1). 10.1001/archpediatrics.2010.247 [DOI] [PubMed] [Google Scholar]
  15. Cuthbert BN, & Insel TR (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine, 11(1), 126. 10.1186/1741-7015-11-126 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. de Arellano MAR, Lyman DR, Jobe-Shields L, George P, Dougherty RH, Daniels AS, Ghose SS, Huang L, & Delphin-Rittmon ME (2014). Trauma-Focused Cognitive-Behavioral Therapy for Children and Adolescents: Assessing the Evidence. Psychiatric Services, 65(5), 591–602. 10.1176/appi.ps.201300255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Deveney CM (2019). Reward processing and irritability in young adults. Biological Psychology, 143, 1–9. 10.1016/j.biopsycho.2019.02.002 [DOI] [PubMed] [Google Scholar]
  18. Diehle J, Opmeer BC, Boer F, Mannarino AP, & Lindauer RJL (2015). Trauma-focused cognitive behavioral therapy or eye movement desensitization and reprocessing: What works in children with posttraumatic stress symptoms? A randomized controlled trial. European Child & Adolescent Psychiatry, 24(2), 227–236. 10.1007/s00787-014-0572-5 [DOI] [PubMed] [Google Scholar]
  19. Dixon ML, Thiruchselvam R, Todd R, & Christoff K (2017). Emotion and the prefrontal cortex: An integrative review. Psychological Bulletin, 143(10), 1033–1081. 10.1037/bul0000096 [DOI] [PubMed] [Google Scholar]
  20. Dougherty LR, Galano MM, Chad-Friedman E, Olino TM, Bufferd SJ, & Klein DN (2021). Using Item Response Theory to Compare Irritability Measures in Early Adolescent and Childhood Samples. Assessment, 28(3), 918–927. 10.1177/1073191120936363 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Dougherty LR, Schwartz KTG, Kryza-Lacombe M, Weisberg J, Spechler PA, & Wiggins JL (2018). Preschool- and School-Age Irritability Predict Reward-Related Brain Function. Journal of the American Academy of Child & Adolescent Psychiatry, 57(6), 407–417.e2. 10.1016/j.jaac.2018.03.012 [DOI] [PubMed] [Google Scholar]
  22. Dugré JR, & Potvin S (2022). Developmental multi-trajectory of irritability, anxiety, and hyperactivity as psychological markers of heterogeneity in childhood aggression. Psychological Medicine, 52(2), 241–250. 10.1017/S0033291720001877 [DOI] [PubMed] [Google Scholar]
  23. Evans SC, Corteselli KA, Edelman A, Scott H, & Weisz JR (2022). Is Irritability a Top Problem in Youth Mental Health Care? A Multi-informant, Multi-method Investigation. Child Psychiatry & Human Development. 10.1007/s10578-021-01301-8 [DOI] [PubMed] [Google Scholar]
  24. Hagler DJ, Hatton SeanN., Cornejo MD, Makowski C, Fair DA, Dick AS, Sutherland MT, Casey BJ, Barch DM, Harms MP, Watts R, Bjork JM, Garavan HP, Hilmer L, Pung CJ, Sicat CS, Kuperman J, Bartsch H, Xue F, … Dale AM (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage, 202, 116091. 10.1016/j.neuroimage.2019.116091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hanson JL, Hariri AR, & Williamson DE (2015). Blunted Ventral Striatum Development in Adolescence Reflects Emotional Neglect and Predicts Depressive Symptoms. Biological Psychiatry, 78(9), 598–605. 10.1016/j.biopsych.2015.05.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Helfinstein SM, Kirwan ML, Benson BE, Hardin MG, Pine DS, Ernst M, & Fox NA (2013). Validation of a child-friendly version of the monetary incentive delay task. Social Cognitive and Affective Neuroscience, 8(6), 720–726. 10.1093/scan/nss057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Herringa RJ (2017). Trauma, PTSD, and the Developing Brain. Current Psychiatry Reports, 19(10), 69. 10.1007/s11920-017-0825-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Herzberg MP, & Gunnar MR (2020). Early life stress and brain function: Activity and connectivity associated with processing emotion and reward. NeuroImage, 209, 116493. 10.1016/j.neuroimage.2019.116493 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hodgdon EA, Yu Q, Kryza‐Lacombe M, Liuzzi MT, Aspe GI, Menacho VC, Bozzetto L, Dougherty L, & Wiggins JL (2021). Irritability‐related neural responses to frustrative nonreward in adolescents with trauma histories: A preliminary investigation. Developmental Psychobiology, 63(6). 10.1002/dev.22167 [DOI] [PubMed] [Google Scholar]
  30. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, & Wang P (2010). Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of Psychiatry, 167(7), 748–751. 10.1176/appi.ajp.2010.09091379 [DOI] [PubMed] [Google Scholar]
  31. Jensen TK, Holt T, Ormhaug SM, Egeland K, Granly L, Hoaas LC, Hukkelberg SS, Indregard T, Stormyren SD, & Wentzel-Larsen T (2014). A Randomized Effectiveness Study Comparing Trauma-Focused Cognitive Behavioral Therapy With Therapy as Usual for Youth. Journal of Clinical Child & Adolescent Psychology, 43(3), 356–369. 10.1080/15374416.2013.822307 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Keding TJ, & Herringa RJ (2016). Paradoxical Prefrontal–Amygdala Recruitment to Angry and Happy Expressions in Pediatric Posttraumatic Stress Disorder. Neuropsychopharmacology, 41(12), 2903–2912. 10.1038/npp.2016.104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kim G, Shin J, & Kim J-W (2021). The mediating role of internalizing and externalizing symptoms in the relationship between childhood trauma and suicidality among adolescents: A structural equation model. Child and Adolescent Psychiatry and Mental Health, 15(1), 79. 10.1186/s13034-021-00434-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Knutson B, Westdorp A, Kaiser E, & Hommer D (2000). FMRI Visualization of Brain Activity during a Monetary Incentive Delay Task. NeuroImage, 12(1), 20–27. 10.1006/nimg.2000.0593 [DOI] [PubMed] [Google Scholar]
  35. Kryza-Lacombe M, Hernandez B, Owen C, Reynolds RC, Wakschlag LS, Dougherty LR, & Wiggins JL (2021). Neural mechanisms of reward processing in adolescent irritability. Developmental Psychobiology, 63(5), 1241–1254. 10.1002/dev.22090 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Magruder KM, McLaughlin KA, & Elmore Borbon DL (2017). Trauma is a public health issue. European Journal of Psychotraumatology, 8(1), 1375338. 10.1080/20008198.2017.1375338 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. McLaren DG, Ries ML, Xu G, & Johnson SC (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches. NeuroImage, 61(4), 1277–1286. 10.1016/j.neuroimage.2012.03.068 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. McLaughlin KA, Koenen KC, Hill ED, Petukhova M, Sampson NA, Zaslavsky AM, & Kessler RC (2013). Trauma exposure and Posttraumatic Stress Disorder in a national sample of adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 52(8), 815–830.e14. 10.1016/j.jaac.2013.05.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. McLaughlin KA, Weissman D, & Bitrán D (2019). Childhood Adversity and Neural Development: A Systematic Review. Annual Review of Developmental Psychology, 1(1), 277–312. 10.1146/annurev-devpsych-121318-084950 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Merikangas KR, He J, Burstein M, Swanson SA, Avenevoli S, Cui L, Benjet C, Georgiades K, & Swendsen J (2010). Lifetime prevalence of mental disorders in U.S. adolescents: Results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A). Journal of the American Academy of Child & Adolescent Psychiatry, 49(10), 980–989. 10.1016/j.jaac.2010.05.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Mulraney MA, Melvin GA, & Tonge BJ (2014). Psychometric properties of the Affective Reactivity Index in Australian adults and adolescents. Psychological Assessment, 26(1), 148–155. 10.1037/a0034891 [DOI] [PubMed] [Google Scholar]
  42. Mulraney M, Melvin G, & Tonge B (2014). Brief report: Can irritability act as a marker of psychopathology? Journal of Adolescence, 37(4), 419–423. 10.1016/j.adolescence.2014.03.005 [DOI] [PubMed] [Google Scholar]
  43. Perlman SB, Jones BM, Wakschlag LS, Axelson D, Birmaher B, & Phillips ML (2015). Neural substrates of child irritability in typically developing and psychiatric populations. Developmental Cognitive Neuroscience, 14, 71–80. 10.1016/j.dcn.2015.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Pierce ZP, & Black JM (2021). The Neurophysiology Behind Trauma-Focused Therapy Modalities Used to Treat Post-Traumatic Stress Disorder Across the Life Course: A Systematic Review. Trauma, Violence, & Abuse, 152483802110484. 10.1177/15248380211048446 [DOI] [PubMed] [Google Scholar]
  45. Sailer U, Robinson S, Fischmeister F. Ph. S., König D, Oppenauer C, Lueger-Schuster B, Moser E, Kryspin-Exner I, & Bauer H (2008). Altered reward processing in the nucleus accumbens and mesial prefrontal cortex of patients with posttraumatic stress disorder. Neuropsychologia, 46(11), 2836–2844. 10.1016/j.neuropsychologia.2008.05.022 [DOI] [PubMed] [Google Scholar]
  46. Sanislow CA, Pine DS, Quinn KJ, Kozak MJ, Garvey MA, Heinssen RK, Wang PS-E, & Cuthbert BN (2010). Developing constructs for psychopathology research: Research domain criteria. Journal of Abnormal Psychology, 119(4), 631–639. 10.1037/a0020909 [DOI] [PubMed] [Google Scholar]
  47. Schermuly-Haupt M-L, Linden M, & Rush AJ (2018). Unwanted Events and Side Effects in Cognitive Behavior Therapy. Cognitive Therapy and Research, 42(3), 219–229. 10.1007/s10608-018-9904-y [DOI] [Google Scholar]
  48. Seidemann R, Duek O, Jia R, Levy I, & Harpaz-Rotem I (2021). The reward system and Post-Traumatic Stress Disorder: Does trauma affect the way we interact with positive stimuli? Chronic Stress, 5, 247054702199600. 10.1177/2470547021996006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Shou H, Yang Z, Satterthwaite TD, Cook PA, Bruce SE, Shinohara RT, Rosenberg B, & Sheline YI (2017). Cognitive behavioral therapy increases amygdala connectivity with the cognitive control network in both MDD and PTSD. NeuroImage: Clinical, 14, 464–470. 10.1016/j.nicl.2017.01.030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Somerville LH, & Casey B (2010). Developmental neurobiology of cognitive control and motivational systems. Current Opinion in Neurobiology, 20(2), 236–241. 10.1016/j.conb.2010.01.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Stringaris A, Goodman R, Ferdinando S, Razdan V, Muhrer E, Leibenluft E, & Brotman MA (2012). The Affective Reactivity Index: A concise irritability scale for clinical and research settings. Journal of Child Psychology and Psychiatry, 53(11), 1109–1117. 10.1111/j.1469-7610.2012.02561.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Vilar A, Pérez-Sola V, Blasco MJ, Pérez-Gallo E, Ballester Coma L, Batlle Vila S, Alonso J, Serrano-Blanco A, & Forero CG (2019). Investigación traslacional en psiquiatría: El marco Research Domain Criteria (RDoC). Revista de Psiquiatría y Salud Mental, 12(3), 187–195. 10.1016/j.rpsm.2018.04.002 [DOI] [PubMed] [Google Scholar]
  53. Villalta L, Smith P, Hickin N, & Stringaris A (2018). Emotion regulation difficulties in traumatized youth: A meta-analysis and conceptual review. European Child & Adolescent Psychiatry, 27(4), 527–544. 10.1007/s00787-018-1105-4 [DOI] [PubMed] [Google Scholar]
  54. Wamser-Nanney R, & Steinzor CE (2017). Factors related to attrition from trauma-focused cognitive behavioral therapy. Child Abuse & Neglect, 66, 73–83. 10.1016/j.chiabu.2016.11.031 [DOI] [PubMed] [Google Scholar]

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Supplementary Materials

Supplement
NIHMS1943180-supplement-Supplement.docx (1.1MB, docx)

Data Availability Statement

Due to the high sensitivity of the data, data with potentially identifying information removed are available from the corresponding author upon written request with the permission of the senior author.

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