# Social problems and brain structure development following childhood mild traumatic brain injury

Fanny Dégeilh, Tilmann von Soest, Lia Ferschmann, Joanne Beer, Malo Gaubert, Inga Koerte, Christian Tamnes

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Fanny Dégeilh, Tilmann von Soest, Lia Ferschmann, Joanne Beer, Malo Gaubert, et al. Social problems and brain structure development following childhood mild traumatic brain injury. Cortex, 2023, 162, pp.26-37. 10.1016/j.cortex.2023.02.003. hal-03922457

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Social problems and brain structure development following childhood mild
traumatic brain injury

Fanny Dégeilh<sup>1,2</sup>, Tilmann von Soest<sup>3</sup>, Lia Ferschmann<sup>3</sup>, Joanne C. Beer<sup>4</sup>, Malo

Gaubert<sup>1,2</sup>, Inga K. Koerte<sup>2,5,6,7 *</sup>, Christian K. Tamnes<sup>3,8,9*</sup>

<sup>1</sup> University of Rennes 1, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN – ERL
U 1228, Rennes, France

<sup>2</sup> Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany

<sup>3</sup> PROMENTA Research Center, Department of Psychology, University of Oslo, Norway

<sup>4</sup> Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States of America

<sup>5</sup> Psychiatric Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America

<sup>6</sup> Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany

<sup>7</sup> Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America

<sup>8</sup> NORMENT, Institute of Clinical Medicine, University of Oslo, Norway

<sup>9</sup> Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway

* Co-last author
**Corresponding author:** Correspondence concerning this article should be addressed
to Fanny Dégeilh, IRISA / Inria Rennes, Empenn, Campus Universitaire de Beaulieu,
35042 Rennes cedex – France. Tel.: +33 2 99 84 71 63. E-mail:

fanny.degeilh@inserm.fr

**Running title:** TBI and social brain development
**Abstract**

Childhood mild traumatic brain injury (mTBI) is associated with elevated risk of developing social problems, which may be underpinned by changes in the structural developmental trajectory of the social brain, a network of cortical regions supporting social cognition and behavior. However, limited sample sizes and cross-sectional designs generally used in neuroimaging studies of pediatric TBI have prevented explorations of this hypothesis. This longitudinal retrospective study examined the development of parent-reported social problems and cortical thickness in social brain regions following childhood mTBI using data from the large population-based Adolescent Brain Cognitive Development (ABCD) Study. Two-group latent change score models revealed different developmental trajectories from ages 10 to 12 years in the level of social problems between children with (n=345) and without (n=7,089) mTBI. Children with mTBI showed higher, but non-clinical, levels of social problems than controls at age 10. Then, social problems decreased over 2 years, but still remained higher, but non-clinical, than in controls in which they stayed stable. Both groups showed similar decreases in social brain cortical thickness between ages 10 and 12 years. Further studies providing detailed information on the injury mechanism and acute symptoms are needed to better understand individual differences in social functioning and brain development in pediatric TBI.

**Keywords:** Traumatic brain injury, Concussion, Brain development, Social problems, Cortical thickness
# Social problems and brain structure development following childhood mild traumatic brain injury

## 1. Introduction

Traumatic brain injury (TBI) is highly prevalent in children and can lead to long-term behavioral problems, including social problems such as aggressive behaviors and social cognitive deficits (Anderson et al., 2022; Catroppa et al., 2015; Dégeilh et al., 2018; Li & Liu, 2013; Ryan et al., 2021; Zamani et al., 2019). Childhood TBI, even of mild severity (mTBI) which represent 90% of TBI cases (Zemek et al., 2013), may profoundly impact the child's quality of life and academic achievements (Anderson et al., 2009). While the consequences of TBI on children's social functions have received increased attention over the past decade, the brain mechanisms underlying the social dysfunction following childhood TBI remain poorly understood (Ryan, Catroppa, Godfrey, et al., 2016).

Social cognitive functions emerge early in development, but also show a protracted development, continuing across childhood and adolescence, coinciding with the extended maturation of the ‘social brain’, an anatomically distributed set of cortical regions underlying social cognition and behavior (Blakemore, 2012; Frith & Frith, 2003; Mills et al., 2014). The social brain comprises the posterior superior temporal sulcus, the anterior temporal cortex, the medial prefrontal cortex (medial Brodmann Area 10), and the inferior parietal cortex (also referred as the temporoparietal junction; Frith and Frith, 2003; Blakemore, 2012; Mills et al., 2014). Cortical thickness decreases in these brain regions from childhood through late adolescence (Mills et al., 2014; Tamnes et al., 2017). Childhood TBI may disrupt the expected
brain structural developmental trajectories of social brain regions, that could manifest
as development of social problems.

Despite the heterogeneity of the brain injury profiles following TBI, common
causes of injury involve similar mechanisms, and some brain regions are more
vulnerable than others to injury, and therefore more commonly damaged (Bigler,
Abildskov, et al., 2013). Particularly vulnerable brain regions include parts of the social
brain network. Indeed, given the shape of the skull and how the brain is held in situ,
focal contusions often occur in frontotemporal regions and lead to a frontotemporal
distribution of cortical atrophy (Bigler, Abildskov, et al., 2013). Moreover, evidence
from neuroimaging studies suggest an association between structural social brain
alterations (i.e., brain lesion, reduced cortical volume or thickness in frontal, temporal,
and parietal regions) and persistent social deficits following pediatric moderate-to-
severe TBI (Bigler, Yeates, et al., 2013; Levan et al., 2015; Ryan, Catroppa, Beare, et
al., 2016; Ryan, Catroppa, Godfrey, et al., 2016; Ryan, van Bijnen, et al., 2016;
Zamani et al., 2019). Concerning mTBI, the very few studies conducted so far show
conflicting results, with some reporting reduced gray matter volumes and cortical
thickness in frontal, temporal and parietal regions following pediatric mTBI (Mayer et
al., 2015; Urban et al., 2017), while others report no changes (King et al., 2019; Ryan,
Catroppa, Beare, et al., 2016).

The interaction between injury physiopathology and brain maturation may
underpin the long-term functional impact of pediatric TBI (King et al., 2019).

Longitudinal studies of brain maturation following pediatric TBI are however scarce.

The three available studies explored longitudinal changes in regional brain volumes and cortical thickness, and suggested deviation from the expected developmental pattern, particularly in frontal and temporal regions, following mTBI or moderate-to-severe TBI
TBI and social brain development

sustained in late childhood and adolescence (Dennis et al., 2017; Mayer et al., 2015;
Wilde et al., 2012). However, the small sample sizes (all Ns < 21 TBI) and analytic
methods not handling challenges associated with longitudinal data, such as partially
missing data, limit the generalization of these findings. In addition, none of these
studies included children who sustained a TBI at a younger age than 8 years.

The present study aimed to characterized longitudinal changes in social
problems and cortical thickness in social brain regions following pediatric mTBI using
data from the large Adolescent Brain Cognitive Development (ABCD) Study (Casey
et al., 2018). We used an analytic method sensitive to inter-individual variability in
intra-individual change, namely latent change score models (LCSM), to test for group
differences in developmental changes in social problems and cortical thickness from
ages 10 years to ages 12 years in 345 children with a history of childhood mTBI and
7,089 controls.

## 2. Methods

No part of the study procedures or analyses was pre-registered prior to the research
being conducted. Below, we report how we determined our sample size, all data
exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were
established prior to data analysis, all manipulations, and all measures in the study.

2.1. Study design

Data used in the current analyses was collected as part of the ABCD Study®. The
ABCD study is a large 21-site longitudinal study of child development in the United
States, which recruited 11,876 children aged 8.9 to 11.1 years between 2016 and 2018
and follow them annually (Barch et al., 2018; Garavan et al., 2018). All ABCD study
procedures were approved by the local human research ethics committees. All
families provided written informed consent for participation.
**2.2. Measures**

The current study used baseline, 1-year follow-up and 2-year follow-up demographic, physical health, mental health (Barch et al., 2018), and neuroimaging (Casey et al., 2018) data from ABCD 4.0 data release ([http://dx.doi.org/10.15154/1527902](http://dx.doi.org/10.15154/1527902)). Only measures included in the current analyses are described here.

**2.2.1. Demographic Variables.**

Demographic variables, including child age at each visit, sex assigned at birth, ethnicity, family income, and parental education (i.e., highest grade or level of school completed or highest degree received), were reported by parents via the demographic questionnaire (Barch et al., 2018).

**2.2.2. Brain injury history.**

The Modified Ohio State University TBI Screen-Short Version (Barch et al., 2018; Bogner et al., 2017; Corrigan & Bogner, 2007) is a parent-report questionnaire designed to measure lifetime history of TBI in children. Parents are asked to recall events related to the occurrence of injury to the head or neck (e.g., car accident, fall, being hit by someone or something). In case of positive response to any occurrence, follow-up questions are asked to determine the age of the injury and whether the injury was associated with loss of consciousness (LOC) or memory loss. At baseline, parents were asked about the child's entire life (i.e., the first 10 years of life). At follow-ups, parents were asked to report on any new injury to the head or neck that may have occurred since the previous visit.

The summary score ('tbi_ss_worst_overall') was used to identify participants as mTBI or controls. The summary score rates injury severity on a five-point scale: (1) Improbable TBI (no TBI or TBI without LOC or memory loss), (2) Possible mild TBI (TBI without LOC, but with memory loss), (3) Mild TBI (TBI with LOC ≤ 30
TBI and social brain development

minutes), (4) Moderate TBI (TBI with LOC between 30 minutes to 24 hours), and (5) Severe TBI (TBI with LOC ≥ 24 hours). Children with a moderate or severe TBI (score > 3) at baseline, who reported sustaining a TBI between baseline and 2-year follow-up visit, or which the score was missing were excluded from the analyses.

Children with a score of 2 or 3 at baseline were included in the mTBI group. Children with a summary score of 1 <u>without</u> history of neck or head injury at baseline or at follow-ups were included in the control group.

2.2.3. Social problems.

Baseline and 2-year follow-up age-corrected t-scores from the social problem subscale on the Child Behavior Checklist-Parent form (CBCL; Achenbach and Rescorla, 2000) were used as measures of children’s social problems. CBCL is a parent report of emotional and behavioral problems in children and adolescents.

Social problems subscale contains 11 items related to several areas of social functioning, including peer rejection (e.g., “Not liked by other kids”, “Feels others are out to get him/her”, “Complains of loneliness”) and social interaction difficulties (e.g., “Clings to adults or too dependent”, “Prefers being with younger kids”). Each item is rated on a three-point rating scale (0 = Not true; 1 = Somewhat or sometimes true; 2 = Very true or often true), and the social problems subscale includes 11 items.

Higher scores suggest more social problems, with score ≤64 corresponding to non-clinical scores, [65-69] corresponding to borderline scores, and ≥ 70 corresponding to clinical scores.

**2.2.4. Social brain cortical thickness.**

Baseline and 2-year follow-up cortical thickness was computed on T1-weighted images by the ABCD group (Hagler et al., 2019) using FreeSurfer v5.3 (Fischl, 2012) within the Desikan-Killiany regions (Desikan et al., 2006). The following left and
TBI and social brain development

right hemisphere regions of interest (ROIs) corresponding to core region of the social
brain network (Mills et al., 2014) were used in our analyses: medial orbitofrontal
cortex, anterior temporal cortex (temporal pole), inferior parietal cortex, and banks of
the superior temporal sulcus (Fig. 1).

<figure><img src="image_2.png" /><figcaption>**Figure 1. The social brain.** Social brain regions of interest. medial orbitofrontal (blue); inferior parietal (yellow); superior temporal sulcus (green); anterior temporal cortex (temporal pole; pink)</figcaption></figure>
Scanner effects (n scanners = 26) were corrected before statistical analyses using longitudinal ComBat (long-ComBat; Beer et al., 2020). Long-ComBat is an empirical Bayesian method for harmonizing longitudinal multi-scanner MRI data removing both additive and multiplicative scanner effects. ComBat is more robust to outliers in the case of small within-scanner sample sizes than other harmonization methods (Johnson et al., 2007). Long-ComBat model included explanatory variables child sex (0 = female; 1 = male) and parental education (ranging from 0 to 18), group, time x group interaction, and a subject-level random intercept. Fig. S1 (see supplemental) shows residual boxplots by scanner before and after data harmonization using long-ComBat.

## 2.3. Statistical analyses

Group differences on demographic variables were tested using Chi-squared test (categorial data) and t-test (continuous data).

Multigroup LCSM (Kievit et al., 2018) were constructed with the lavaan 0.6-12 package (Rosseel, 2012) in R (version R4.1.2) to estimate latent change scores between baseline and follow-up for social problems and cortical thickness (for each ROI – Combat-corrected cortical thickness values) for all individuals (see Fig. 2). Group (mTBI versus controls) differences in four parameters of interest – the mean of the baseline score for behavior or cortical thickness, the mean of the latent change factor (reflecting rate of change over time), the variance of the baseline score (reflecting individual differences in scores at baseline) and the variance of the latent change factors (reflecting individual differences in rate of change) – were tested by means of equality constraints (Kievit et al., 2018). More specifically, each parameter of interest was individually constrained to be equal in the two groups and this constrained model was compared to a model where the parameter of interest was freely estimated using scaled
Chi-squared difference tests (Satorra & Bentler, 2001). A significant difference between the two models reflects that the constrained parameter is significantly different between the two groups. Both the baseline measure and the latent change factor were controlled for child sex (0 = female; 1 = male) and parental education (ranging from 0 to 18). Covariates included in the models were selected a priory – before any group comparison – based on previous work published that reported sex and socio-economic status (family income or parent education) impact on child’s recovery and outcomes following childhood TBI (Gerring & Wade, 2012; Gupte et al., 2019), or brain development (DeCasien et al., 2022; Farah, 2017). Parental education was preferred to family income as a socio-economic status proxy as it in the ABCD study is a continuous variable and has less missing data than the income variable (0.1% of missing data for the parental education variable vs. 8% for the income variable).

When the multigroup LCSM reveled significant results, a secondary univariate
LCSM was constricted to the mTBI group to explore if the changes observed following
mTBI were associated with age at injury.
<figure><img src="image_3.png" /><figcaption>**Figure 2: Latent Change Score Model.** Social problems and cortical thickness (X) measured at baseline and at 2-year follow-up. Change (Δ) in X across time is modeled as latent variable. Sex and parental education were entered as covariates.</figcaption></figure>
**3. Results**

**3.1. Participants final sample**

From the 11,876 participants of ABCD data release 4.0, 4,318 were not eligible for the current study or met an exclusion criterion, 351 children were categorized as mTBI and 7,207 children were categorized as controls (Fig. 3). Of these 7,558 eligible participants, 124 were excluded from the analyses because of the following criteria: Structural MRI data were missing at both timepoint for 79 controls and 2 mTBI, and did not pass the ABCD quality control (Hagler et al., 2019) for 15 controls. To reduce imbalance in scanners between the two groups, 15 controls were excluded because their MRI data were collected on a scanner used for acquiring less than two scans per group (Beer et al., 2020). One participant was mislabeled as mTBI, and 3 had repetitive head impact. Finally, parental education (used as covariate in the analyses) was missing for 9 controls. The final sample for our analyses thus included 345 children with mTBI and 7,089 controls (cf. Table 1 for demographic information and group comparison). The two groups were significantly different regarding sex and parental education; thus, these two variables were entered as covariates in the analyses.
<figure><img src="image_4.png" /><figcaption>Figure 3: Participants selection.</figcaption></figure>

Note: 'Head or neck injury' refers to children with a Modified Ohio State University TBI Screen-Short Version summary score of 1 (improbable TBI) *with* history of neck or head injury at baseline or at follow-ups.
<table><caption>Table 1: Demographic characteristics of the final sample</caption><thead><tr><th></th><th>TBI<br>N = 345</th><th>Controls<br>N = 7,089</th><th>t/χ<sup>2</sup></th><th>p</th></tr></thead><tbody><tr><td>Age at baseline (years), M (SD)</td><td>10.0 (0.6)</td><td>10.0 (0.6)</td><td>-1.7</td><td>.08</td></tr><tr><td>Age at 2-year follow-up (years), M (SD)</td><td>11.9 (0.6)</td><td>11.9 (0.6)</td><td>-1.1</td><td>.27</td></tr><tr><td>Age at injury (years), M (SD)</td><td>5.9 (2.8)</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Months between MRI visits, M (SD)</td><td>24.6(2.3)</td><td>24.4 (2.3)</td><td>-0.7</td><td>.48</td></tr><tr><td>Sex</td><td>-</td><td>-</td><td>10.9</td><td><strong>&lt;.0001</strong></td></tr><tr><td>Male, n (%)</td><td>200 (58)</td><td>3,462 (49)</td><td>-</td><td>-</td></tr><tr><td>Female, n (%)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Parent education (years), M (SD)</td><td>17.0 (2.5)</td><td>16.4 (2.7)</td><td>-2.5</td><td><strong>&lt;.0001</strong></td></tr><tr><td>Family income</td><td>-</td><td>-</td><td>19.4</td><td><strong>.05</strong></td></tr><tr><td>Less than $5,000</td><td>6 (1.7)</td><td>225 (3.2)</td><td>-</td><td>-</td></tr><tr><td>$5,000 through $11,999</td><td>6 (1.7)</td><td>237 (3.3)</td><td>-</td><td>-</td></tr><tr><td>$12,000 through $15,999</td><td>3 (0.9)</td><td>149 (2.1)</td><td>-</td><td>-</td></tr><tr><td>$16,000 through $24,999</td><td>12 (3.5)</td><td>310 (4.4)</td><td>-</td><td>-</td></tr><tr><td>$25,000 through $34,999</td><td>17 (4.9)</td><td>386 (5.4)</td><td>-</td><td>-</td></tr><tr><td>$35,000 through $49,999</td><td>21 (6.1)</td><td>583 (8.2)</td><td>-</td><td>-</td></tr><tr><td>$50,000 through $74,999</td><td>43 (12.5)</td><td>891 (12.6)</td><td>-</td><td>-</td></tr><tr><td>$75,000 through $99,999</td><td>45 (13.0)</td><td>991 (14.0)</td><td>-</td><td>-</td></tr><tr><td>$100,000 through $199,999</td><td>121 (35.1)</td><td>2022 (28.5)</td><td>-</td><td>-</td></tr><tr><td>$200,000 and greater</td><td>46 (13.3)</td><td>715 (10.1)</td><td>-</td><td>-</td></tr><tr><td>Not answered or not known, n (%)</td><td>25 (7.2)</td><td>580 (8.2)</td><td>-</td><td>-</td></tr><tr><td>Ethnicity</td><td>-</td><td>-</td><td>26.1</td><td><strong>.04</strong></td></tr><tr><td>White, n (%)</td><td>256 (74.2)</td><td>4524 (63.8)</td><td>-</td><td>-</td></tr><tr><td>Black/African American, n (%)</td><td>28 (8.1)</td><td>1094 (15.4)</td><td>-</td><td>-</td></tr><tr><td>American Indian, n (%)</td><td>0 (0)</td><td>27 (0.4)</td><td>-</td><td>-</td></tr><tr><td>Native Hawaiian, n (%)</td><td>0 (0)</td><td>1 (0.01)</td><td>-</td><td>-</td></tr><tr><td>Samoan, n (%)</td><td>0 (0)</td><td>3 (0.04)</td><td>-</td><td>-</td></tr><tr><td>Other Pacific Islander, n (%)</td><td>0 (0)</td><td>9 (0.1)</td><td>-</td><td>-</td></tr><tr><td>Asian Indian, n (%)</td><td>1 (0.3)</td><td>30 (0.4)</td><td>-</td><td>-</td></tr><tr><td>Chinese, n (%)</td><td>1 (0.3)</td><td>46 (0.6)</td><td>-</td><td>-</td></tr><tr><td>Filipino, n (%)</td><td>3 (0.9)</td><td>30 (0.4)</td><td>-</td><td>-</td></tr><tr><td>Japanese, n (%)</td><td>0 (0)</td><td>8 (0.1)</td><td>-</td><td>-</td></tr><tr><td>Korean, n (%)</td><td>0 (0)</td><td>10 (0.1)</td><td>-</td><td>-</td></tr><tr><td>Vietnamese, n (%)</td><td>0 (0)</td><td>14 (0.2)</td><td>-</td><td>-</td></tr><tr><td>Other Asian, n (%)</td><td>0 (0)</td><td>23 (0.3)</td><td>-</td><td>-</td></tr><tr><td>Other, n (%)</td><td>18 (5.2)</td><td>313 (4.4)</td><td>-</td><td>-</td></tr><tr><td>Multiple, n (%)</td><td>34 (10.0)</td><td>849 (12.0)</td><td>-</td><td>-</td></tr><tr><td>Not answered or not known, n (%)</td><td>4 (1.1)</td><td>108 (1.5)</td><td>-</td><td>-</td></tr></tbody></table>
**3.2. Social problems**

The two-group LCSM without constraints across groups (i.e., all parameters freely estimated) fits the data well: $\chi^2(4) = 19.12$, Comparative Fit Index (CFI) = 0.99, Turkey-Lewis Index (TLI) = 0.98, Root Mean Square Error of Approximation (RMSEA) = 0.032 [0.019 0.047]. Constraining the parameters of interest one by one to be equal across groups significantly reduced the model fit in all models (all $p_s \le .002$) reflecting group differences in both means and variances of the baseline value of and change in social problems (Table 2; Fig. 4).

Children with a history of mTBI showed higher levels of social problems at baseline (estimated mean = 56.4) than the control group (estimated mean = 54.4; Table 3), with levels of social problems for both groups falling in the non-clinical range. Moreover, social problems score decreased faster in children with history of mTBI (estimated mean = -1.2, *p* = .007) than in controls in which social problems score stayed stable over time (estimated mean: -0.3, *p* = .33; Table 3). A linear regression model accounting for sex and parental education revealed that social problems score at 2-year follow-up remained significantly higher by 1.3 +/- 0.3 points (*p* < .0001) in children with an history of mTBI than in controls (estimated mean = 54.7 +/- 0.4), still falling in the non-clinical range for both groups. In addition, the proportion of children with clinically significant social problems score was higher in mTBI than in controls at both timepoint (Table 4).

Children with mTBI showed greater individual difference at baseline (variance = 45.7) and in change (variance = 26.9) than the controls (baseline variance = 17.8, change variance = 15.6; Table 3). Of note, the results were similar when covariates (sex and parental education) were not included in the model (see supplemental Tables S1 and S2).
Sex had significant association with both baseline levels and change in social problems (all *ps* <.0001; Table 3). Males showed higher baseline scores and faster decrease over time in social problems than females. Parent education had a negative significant association with baseline levels in social problems (all *ps* <.0001; Table 3).

Secondary univariate LCSM in the mTBI group revealed no association between the age at injury and the means of the baseline value (p = .20) of and change in (p = .63) social problems.
<table><caption>Table 2: Social problems - Model fit comparisons</caption><thead><tr><th></th><th>df</th><th>χ<sup>2</sup></th><th>χ<sup>2</sup>Δ</th><th>p</th></tr></thead><tbody><tr><td>All parameters freely estimated</td><td>4</td><td>19.46</td><td></td><td></td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>47.21</td><td>87.35</td><td>&lt;.0001</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>215.02</td><td>85.78</td><td>&lt;.0001</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>27.24</td><td>9.93</td><td>.002</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td>66.49</td><td>14.76</td><td>.0001</td></tr></tbody></table>

<table><caption>Table 3: Social problems – Model estimates (all parameters freely estimated)</caption><thead><tr><th></th><th>Controls</th><th>TBI</th></tr></thead><tbody><tr><td>Baseline means</td><td>54.41*</td><td>56.38*</td></tr><tr><td>Baseline variances</td><td>17.83*</td><td>45.70*</td></tr><tr><td>Latent change factor means</td><td>-0.29</td><td>-1.16*</td></tr><tr><td>Latent change factor variance</td><td>15.65*</td><td>26.93*</td></tr><tr><td>Baseline regressed on sex (β)</td><td>0.72*</td><td></td></tr><tr><td>Change regressed on sex (β)</td><td>-0.37*</td><td></td></tr><tr><td>Baseline regressed on parental education (β)</td><td>-0.18*</td><td></td></tr><tr><td>Change regressed on parental education (β)</td><td>0.03</td><td></td></tr></tbody></table>

<table><caption>Table 4: Proportion of children with borderline, clinical, and non-clinical social problems score in each group and at each timepoint</caption><thead><tr><th></th><th>Non-clinical<br>(≤64)</th><th>Borderline<br>([65-69])</th><th>Clinical<br>(>=70)</th><th>&lt;NA&gt;</th><th>χ<sup>2</sup></th><th>p</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center; font-weight: bold;">Baseline</td></tr><tr><td>TBI, n (%)</td><td>309 (89.57)</td><td>17 (4.93)</td><td>19 (5.51)</td><td>0 (0)</td><td>66.03</td><td>&lt;.0001</td></tr><tr><td>Controls, n (%)</td><td>6,883 (97.09)</td><td>124 (1.75)</td><td>81 (1.14)</td><td>1 (0.01)</td><td></td><td></td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">2-year follow-up</td></tr><tr><td>TBI, n (%)</td><td>250 (72.46)</td><td>9 (2.61)</td><td>10 (2.90)</td><td>76 (22.18)</td><td>18.62</td><td>&lt;.0001</td></tr><tr><td>Controls, n (%)</td><td>5,329 (75.17)</td><td>134 (1.89)</td><td>54 (0.76)</td><td>1,572 (22.03)</td><td></td><td></td></tr></tbody></table>

<footnote>* p-value < .05.</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Visualization of changes in social problems scores in controls (CTL) and mTBI children. Control (blue line) and mTBI (yellow line) groups mean trajectory and standard deviation (grey).</figcaption></figure>
3.3. Social brain cortical thickness

For all ROIs (medial orbitofrontal cortex, anterior temporal cortex, inferior parietal cortex, banks of the superior temporal sulcus), the two-group latent change score model without constraints across groups fit the data well: CFI were all equal to 1.00, TLI were between 0.99 and 1.00, and RMSEA were between 0.000 and 0.016 (Table 5). For all ROIs, but the left medial orbitofrontal cortex, constraining the parameters of interest to be equal between groups did not significantly reduce the model fit (all ps ≥ .12), reflecting no group difference on means and variances of the baseline value and change of the social brain cortical thickness (Table 6; Figure 5). Concerning the left medial orbitofrontal cortex, constraining the latent change factor variance to be equal across groups significantly reduced the model fit (p = .03), reflecting group differences in the variances of change in cortical thickness (Table 6; Figure 5).

Children with mTBI showed significantly lower latent change variance (0.011) than controls (0.014; Table 7).
<table><caption>Table 5: Social brain cortical thickness – Model fit (all parameters freely estimated)</caption><thead><tr><th></th><th>χ<sup>2</sup> (df = 4)</th><th>CFI</th><th>TLI</th><th>RMSEA [90CI]</th></tr></thead><tbody><tr><td>Left banks of the superior temporal sulcus</td><td>3.82</td><td>1.00</td><td>1.00</td><td>0.000 [0.000 0.019]</td></tr><tr><td>Right banks of the superior temporal sulcus</td><td>1.86</td><td>1.00</td><td>1.00</td><td>0.004 [0.000 0.013]</td></tr><tr><td>Left medial orbitofrontal cortex</td><td>5.27</td><td>1.00</td><td>0.99</td><td>0.016 [0.000 0.034]</td></tr><tr><td>Right medial orbitofrontal cortex</td><td>4.31</td><td>1.00</td><td>1.00</td><td>0.007 [0.000 0.027]</td></tr><tr><td>Left inferior parietal cortex</td><td>4.15</td><td>1.00</td><td>1.00</td><td>0.000 [0.000 0.019]</td></tr><tr><td>Right inferior parietal cortex</td><td>2.43</td><td>1.00</td><td>1.00</td><td>0.011 [0.000 0.029]</td></tr><tr><td>Left anterior temporal cortex</td><td>4.92</td><td>1.00</td><td>0.99</td><td>0.015 [0.000 0.033]</td></tr><tr><td>Right anterior temporal cortex</td><td>3.99</td><td>1.00</td><td>0.99</td><td>0.011 [0.000 0.030]</td></tr></tbody></table>

Abbreviations: CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of

Approximation; TLI, Turkey-Lewis Index; 90CI, 90% confidence interval.
<table><caption>Table 6: Social brain cortical thickness - Models fit comparisons</caption><thead><tr><th></th><th>Df</th><th>χ<sup>2</sup></th><th>χ<sup>2</sup>Δ</th><th>p</th></tr></thead><tbody><tr><td colspan="5"><strong>Left banks of the superior temporal sulcus</strong></td></tr><tr><td>All parameters freely estimated</td><td>4</td><td>2.51</td><td>-</td><td>-</td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>3.35</td><td>0.86</td><td>.48</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>2.62</td><td>0.11</td><td>.34</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>3.31</td><td>0.08</td><td>.37</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td>3.19</td><td>0.26</td><td>.61</td></tr><tr><td colspan="5"><strong>Right banks of the superior temporal sulcus</strong></td></tr><tr><td>All parameters freely estimated</td><td>4</td><td>4.14</td><td>-</td><td>-</td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>5.10</td><td>0.98</td><td>.32</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>2.12</td><td>0.19</td><td>.66</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>4.23</td><td>0.09</td><td>.76</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td>8.18</td><td>2.35</td><td>.12</td></tr><tr><td colspan="5"><strong>Left medial orbitofrontal cortex</strong></td></tr><tr><td>All parameters freely estimated</td><td>4</td><td>7.13</td><td>-</td><td>-</td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>7.21</td><td>0.08</td><td>.78</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>7.16</td><td>0.03</td><td>.87</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>7.23</td><td>0.09</td><td>.76</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td><strong>12.76</strong></td><td><strong>4.56</strong></td><td><strong>.03</strong></td></tr><tr><td colspan="5"><strong>Right medial orbitofrontal cortex</strong></td></tr><tr><td>All parameters freely estimated</td><td>4</td><td>4.47</td><td>-</td><td>-</td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>4.96</td><td>0.49</td><td>.48</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>4.48</td><td>0.007</td><td>.93</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>4.52</td><td>0.04</td><td>.83</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td>4.83</td><td>0.31</td><td>.57</td></tr><tr><td colspan="5"><strong>Left inferior parietal cortex</strong></td></tr><tr><td>All parameters freely estimated</td><td>4</td><td>1.65</td><td>-</td><td>-</td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>2.74</td><td>1.06</td><td>.30</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>3.62.83</td><td>0.87</td><td>.35</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>1.68</td><td>0.02</td><td>.87</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td>3.06</td><td>0.48</td><td>.49</td></tr><tr><td colspan="5"><strong>Right inferior parietal cortex</strong></td></tr><tr><td>All parameters freely estimated</td><td>4</td><td>5.57</td><td>-</td><td>-</td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>5.62</td><td>0.05</td><td>.82</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>7.57</td><td>1.54</td><td>.21</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>6.20</td><td>0.68</td><td>.41</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td>6.28</td><td>0.33</td><td>.56</td></tr></tbody></table>
<table><thead><tr><th colspan="5">Left anterior temporal cortex</th></tr></thead><tbody><tr><td>All parameters freely estimated</td><td>4</td><td>6.29</td><td>-</td><td>-</td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>6.34</td><td>0.06</td><td>.81</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>7.46</td><td>0.80</td><td>.53</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>6.83</td><td>0.58</td><td>.45</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td>6.40</td><td>0.03</td><td>.86</td></tr></tbody><thead><tr><th colspan="5">Right anterior temporal cortex</th></tr></thead><tbody><tr><td>All parameters freely estimated</td><td>4</td><td>5.04</td><td>-</td><td>-</td></tr><tr><td>Baseline means constrained across groups</td><td>5</td><td>5.05</td><td>0.007</td><td>.93</td></tr><tr><td>Baseline variances constrained across groups</td><td>5</td><td>5.75</td><td>0.25</td><td>.61</td></tr><tr><td>Latent change factor means constrained across groups</td><td>5</td><td>5.05</td><td>0.006</td><td>.94</td></tr><tr><td>Latent change factor variance constrained across groups</td><td>5</td><td>5.53</td><td>0.25</td><td>.62</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>B Right banks of the superior temporal sulcus</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>C Left medial orbitofrontal cortex</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>D Right medial orbitofrontal cortex</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>E Left inferior parietal cortex</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>F Right inferior parietal cortex</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>G Left anterior temporal cortex</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>H Right anterior temporal cortex</figcaption></figure>

<figure><img src="image_13.png" /></figure>

Group CTL mTBI

**Figure 5.** Visualization of changes in social brain cortical thickness in control (CTL) and mTBI children. Control (blue line) and mTBI (yellow line) groups mean trajectory and standard deviation (grey).
In both group and for all ROIs, except for the left and right anterior temporal cortex, thickness significantly decreased by 0.06 to 0.08 mm over the two years of the study period (Table 7). Sex was significantly associated with cortical thickness at baseline in the left and right banks of the superior temporal sulcus, inferior parietal cortex, and anterior temporal cortex, with males showing thicker cortex than females (Table 7). There were also significant sex differences in the rate of change of cortical thickness bilaterally in the banks of the superior temporal sulcus, the inferior parietal cortex, and the medial orbitofrontal cortex, with a faster decrease in males than in females. Parental education was significantly associated with cortical thickness in all ROIs at baseline, with higher parental education associated with thicker cortex (Table 7). Parental education was also significantly associated with the rate of change of cortical thickness in the left and right banks of the superior temporal sulcus and the left medial orbitofrontal cortex, with higher parental education associated with lower decrease.

Of note, the results were similar when covariates were not entered in the model (Table S3), and the analyses were conducted on the raw (not harmonized for the scanners) MRI data (Table S4). Partial correlations accounting for sex and parental education, revealed no significant association between cortical thickness and social problems (r = [-.02 - .01], all *ps* > .07).
<table><caption>Table 7: Social brain cortical thickness – Model estimates (all parameters freely estimated)</caption><thead><tr><th></th><th>Left</th><th>Right</th></tr></thead><tbody><tr><td colspan="3"><strong>Banks of the superior temporal sulcus</strong></td></tr><tr><td>Baseline means</td><td><strong>2.68*</strong></td><td><strong>2.78*</strong></td></tr><tr><td>Baseline variances</td><td><strong>0.02*</strong></td><td><strong>0.03*</strong></td></tr><tr><td>Latent change factor means</td><td><strong>-0.07*</strong></td><td><strong>-0.06*</strong></td></tr><tr><td>Latent change factor variance</td><td><strong>0.01*</strong></td><td><strong>0.01*</strong></td></tr><tr><td>Baseline regressed on sex (β)</td><td><strong>-0.02*</strong></td><td><strong>-0.01*</strong></td></tr><tr><td>Change regressed on sex (β)</td><td><strong>0.01*</strong></td><td><strong>0.02*</strong></td></tr><tr><td>Baseline regressed on parental education (β)</td><td><strong>0.004*</strong></td><td><strong>0.005*</strong></td></tr><tr><td>Change regressed on parental education (β)</td><td><strong>0.001*</strong></td><td><strong>0.001*</strong></td></tr><tr><td colspan="3"><strong>Medial orbitofrontal cortex</strong></td></tr><tr><td>Baseline means</td><td><strong>2.89*</strong></td><td><strong>2.83*</strong></td></tr><tr><td>Baseline variances</td><td><strong>0.02*</strong></td><td><strong>0.02*</strong></td></tr><tr><td>Latent change factor means</td><td><strong>-0.08*</strong></td><td><strong>-0.06*</strong></td></tr><tr><td>Latent change factor variance</td><td><strong>0.01*</strong><sup>b</sup></td><td><strong>0.02*</strong></td></tr><tr><td>Baseline regressed on sex (β)</td><td>0.003</td><td>-0.005</td></tr><tr><td>Change regressed on sex (β)</td><td><strong>0.01*</strong></td><td><strong>0.01*</strong></td></tr><tr><td>Baseline regressed on parental education (β)</td><td><strong>0.002*</strong></td><td><strong>0.002*</strong></td></tr><tr><td>Change regressed on parental education (β)</td><td><strong>0.002*</strong></td><td>0.001</td></tr></tbody></table>

<table><thead><tr><th></th><th>Left</th><th>Right</th></tr></thead><tbody><tr><td colspan="3"><strong>Inferior parietal cortex</strong></td></tr><tr><td>Baseline means</td><td><strong>2.70*</strong></td><td><strong>2.73*</strong></td></tr><tr><td>Baseline variances</td><td><strong>0.01*</strong></td><td><strong>0.01*</strong></td></tr><tr><td>Latent change factor means</td><td><strong>-0.06*</strong></td><td><strong>-0.06*</strong></td></tr><tr><td>Latent change factor variance</td><td><strong>0.01*</strong></td><td><strong>0.01*</strong></td></tr><tr><td>Baseline regressed on sex (β)</td><td><strong>-0.02*</strong></td><td><strong>-0.02*</strong></td></tr><tr><td>Change regressed on sex (β)</td><td><strong>0.02*</strong></td><td><strong>0.02*</strong></td></tr><tr><td>Baseline regressed on parental education (β)</td><td><strong>0.003*</strong></td><td><strong>0.002*</strong></td></tr><tr><td>Change regressed on parental education (β)</td><td>0.001</td><td>0.00</td></tr><tr><td colspan="3"><strong>Anterior temporal cortex</strong></td></tr><tr><td>Baseline means</td><td><strong>3.57*</strong></td><td><strong>3.71*</strong></td></tr><tr><td>Baseline variances</td><td><strong>0.10*</strong></td><td><strong>0.09*</strong></td></tr><tr><td>Latent change factor means</td><td>-0.03</td><td>-0.03</td></tr><tr><td>Latent change factor variance</td><td><strong>0.10*</strong></td><td><strong>0.10*</strong></td></tr><tr><td>Baseline regressed on sex (β)</td><td><strong>-0.02*</strong></td><td><strong>-0.02*</strong></td></tr><tr><td>Change regressed on sex (β)</td><td>-0.01</td><td>0.01</td></tr><tr><td>Baseline regressed on parental education (β)</td><td><strong>0.01*</strong></td><td><strong>0.005*</strong></td></tr><tr><td>Change regressed on parental education (β)</td><td>0.001</td><td>0.002</td></tr></tbody></table>

<footnote>* p-value < .05. <sup>b</sup>TBI (0.011) significantly lower than controls (0.014).</footnote>
**4. Discussion**

This study examined the longitudinal development of social problems and cortical thickness in social brain regions following childhood mTBI. Using a large sample with retrospective information on history of TBI and multigroup latent change score models, an analytic method sensitive to longitudinal change at both individual and group level, we found different developmental changes in the levels of social problems between children with and without history of mTBI. Children with mTBI showed higher levels of social problems than children without mTBI at age 10, although remaining mostly within the non-clinical range. Then, the level of social problems decreased over 2 years but remained higher, but still mostly within the non-clinical range, in children with mTBI than in controls in which they stayed stable. No group difference in the trajectory of the social brain cortical thickness was observed. Cortical thickness significantly decreased between age 10 and 12 years in both groups, except for the anterior temporal cortex where the thickness remained stable over the 2 years.

The present behavioral findings are consistent with previous studies showing persistent differences in social functioning in children with TBI compared to their non-injured peers (Anderson et al., 2022; Catroppa et al., 2015; Dégeilh et al., 2018; Li & Liu, 2013; Ryan et al., 2021; Zamani et al., 2019). Here, we further observed that mTBI during childhood was associated with higher, but non-clinical, levels of social problems, with a decrease over time that may suggest a slow return to a level similar to their non-injured peers. Additional follow-up of the ABCD cohort will allow to test this hypothesis and explore whether or not adolescents with mTBI reach a similar level as their non-injured peers by the end of the adolescence.

While it is believed that the brain development and integrity may underpin social impairments developing following a pediatric TBI (Beauchamp & Anderson, 2010a), no significant difference in the developmental trajectory of the cortical thickness in the social
brain was observed in the current study. This result echoes recent findings by Anderson and colleagues (Anderson et al., 2022) showing no difference in gray matter volume of large-scale brain networks (i.e., Central Executive Network, Default Mode Network, Mentalizing Network, Mirror Neuron Empathy Network, and Salience Network) 3 to 8 weeks post-injury between TBI children with different profiles of social impairment recovery trajectory.

However, it is not possible to conclude whether the absence of significant difference reflects recovery or absence of social brain cortical alteration following childhood mTBI, or that the assessment (here cortical thickness from T1-w) is not sensitive enough to detect existing alteration. Future studies should explore if changes in social brain function or other brain structure metrics may underlie the changes in the level of social problems after pediatric TBI.

In both groups, cortical thickness significantly decreased over the two years of the study in all the regions of the social brain but the anterior temporal cortex. This developmental trajectory of the social brain structure is consistent with what has been observed in longitudinal studies showing that the cerebral cortex undergoes widespread and regionally variable nonlinear decreases in thickness across childhood and adolescence (Mills et al., 2014; Tamnes et al., 2017). Concerning the social brain regions, cortical thickness in the posterior superior temporal sulcus, the medial prefrontal cortex, and the inferior parietal cortex has in previous studies consistently been found to decreased with age, while the findings for the anterior temporal cortex are less consistent, with both increase and decrease observed.

The present study was strengthened by its large sample of children who sustained a mTBI (n = 225, against n < 21 in published neuroimaging studies) between ages 0 to 9 years old (none of the published neuroimaging studies included children who sustained a mTBI before aged of 8 years old), and its longitudinal design. We applied a longitudinal analytic method well-suited to model individual differences in change across two time points and to
handle missing data by means of full information maximum likelihood estimation.

Furthermore, special attention was paid to scanner effects correction using a suitable method
for longitudinal data.

Nonetheless, the current results must be interpreted in the context of some study
limitations. Most notably, the assessment of TBI status was based on parent retrospective
report and thus reflects parents' memory of their child lifetime history of TBI, which is
subject to recall bias in parental response to the questionnaire. In addition, the Modified Ohio
State University TBI Screen-Short Version provides only an indirect index of child TBI
history with limited information on mechanisms of injury and acute symptoms. Furthermore,
the current mTBI definition (from Modified Ohio State University TBI Screen) is based on
LOC or memory loss duration allowing to identify only children having experienced more
severe mTBI (complicated mTBI) and does not allow to identify children having experienced
head or neck injury (excluded from the current analyses) in the absence of alterations in
mental status for which acute symptoms is need to conclude on the presence or absence of a
mTBI diagnose. Given the absence of social problems measures prior to TBI, the possibility
that higher social problems were already present in children prior to TBI cannot be excluded.
Prospective longitudinal studies with more detailed information on the mechanisms and type
of injury, acute symptoms and brain lesion, and objective assessment of the injury severity
(i.e., Glasgow coma scale, symptoms measurement) providing a detailed picture of the
pediatric TBI heterogeneity, are needed to better understand the neural mechanisms
underpinning social impairment after a pediatric TBI. Finally, external (e.g., SES, ethnicity,
or culture) and internal family factors (e.g., parent-child interactions, parental practices, or
family functioning) are known to play a particularly important role in social (Beauchamp &
Anderson, 2010b; Tuerk et al., 2021) and brain (Farah, 2017; Ilyka et al., 2021) development,
as well as to both influence child's recovery following TBI (Gerring & Wade, 2012) and be
TBI and social brain development

impacted by pediatric TBI (Lalonde et al., 2016, 2020). Here we observed higher levels of
social problems in children with mTBI compared to non-injured peers, beyond external
family factors (i.e., parental education). Further studies should explore the role of internal
family factors (e.g., parent-child interaction) on social problems development following
childhood TBI.
**Data availability**

The data that support the findings are available from the NIMH Data Archive (NDA). The ABCD data used in this report came from the ABCD 3.0 data release [NIMH Data Archive Digital Object Identifier (DOI): http://dx.doi.org/10.15154/1527902].

**Code availability**

The code that supports the findings of this study is available as supplementary material.

**Acknowledgments**

**Funding:** This work was supported by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant scheme [#101024023, to F.D.], the European Research Council under the ERC Starting Grant scheme [#804326, to I.K.K.], and the Research Council of Norway [#288083].

**ABCD Study:** Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development<sup>SM</sup> (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at
https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

## Authors Contributions

FD conceptualized the study, conducted the analyses and drafted the original manuscript.

TvS contributed to defining the statistical models, conducting the analyses and writing the manuscript. LF contributed to conducting the analyses and commented on the manuscript. JB contributed the data processing and commented on the manuscript. MG organized the data collection and commented on the manuscript. IKK contributed to conceptualizing the study, provided access to the data, and commented on the manuscript. CKT contributed to conceptualizing the study and writing the manuscript, and supervised the project. All authors contributed to the editing of the paper.

## Competing Interests

IKK receives grant funding from the National Institutes of Health, The European Research Council, and the German Ministry for Research and Education. She receives funding for a collaborative project and serves as a paid scientific advisor for Abbott. She receives royalties for scientific book chapters. Her spouse is an employee at Siemens AG. The other authors declare no competing interests.
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Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., Garavan, H., Orr, C. A., Wager, T. D., Banich, M. T., Speer, N. K., Sutherland, M. T., Riedel, M. C., Dick, A. S., Bjork, J. M., Thomas, K. M., ... Dale, A. M. (2018). The Adolescent Brain
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Catroppa, C., Anderson, V., Beauchamp, M. H., & Yeates, K. O. (2015). *New frontiers in pediatric traumatic brain injury: an evidence base for clinical practice*. Routledge.

Corrigan, J. D., & Bogner, J. (2007). Initial reliability and validity of the Ohio state university TBI identification method. *Journal of Head Trauma Rehabilitation*, 22(6), 318–329. https://doi.org/10.1097/01.HTR.0000300227.67748.77

DeCasien, A. R., Guma, E., Liu, S., & Raznahan, A. (2022). Sex differences in the human brain: a roadmap for more careful analysis and interpretation of a biological reality. *Biology of Sex Differences* 2022 13:1, 13(1), 1–21.
https://doi.org/10.1186/S13293-022-00448-W

Dégeilh, F., Bernier, A., Gravel, J., & Beauchamp, M. H. (2018). Developmental trajectories of adaptive functioning following early mild traumatic brain injury. *Developmental Psychobiology*, 60(8), 1037–1047.
https://doi.org/10.1002/dev.21786

Dennis, E. L., Faskowitz, J., Rashid, F., Babikian, T., Mink, R., Babbitt, C., Johnson, J., Giza, C. C., Jahanshad, N., Thompson, P. M., & Asarnow, R. F. (2017). Diverging volumetric trajectories following pediatric traumatic brain injury. *NeuroImage: Clinical*, 15, 125–135. https://doi.org/10.1016/j.nicl.2017.03.014

Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. *NeuroImage*.
https://doi.org/10.1016/j.neuroimage.2006.01.021

Farah, M. J. (2017). The Neuroscience of Socioeconomic Status: Correlates, Causes, and Consequences. *Neuron*, 96(1), 56–71.
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Fischl, B. (2012). FreeSurfer. *NeuroImage*, 62(2), 774–781.
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Frith, U., & Frith, C. D. (2003). Development and neurophysiology of mentalizing. *Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences*, 358(1431), 459–473. https://doi.org/10.1098/rstb.2002.1218

Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R. Z., Heeringa, S., Jernigan, T., Potter, A., Thompson, W., & Zahs, D. (2018). Recruiting the ABCD sample: Design considerations and procedures. *Developmental Cognitive Neuroscience*, 32, 16–22. https://doi.org/10.1016/J.DCN.2018.04.004

Gerring, J. P., & Wade, S. (2012). The Essential Role of Psychosocial Risk and Protective Factors in Pediatric Traumatic Brain Injury Research. *Journal of Neurotrauma*, 29(4), 621. https://doi.org/10.1089/NEU.2011.2234

Gupte, R., Brooks, W., Vukas, R., Pierce, J., & Harris, J. (2019). Sex Differences in Traumatic Brain Injury: What We Know and What We Should Know. *Journal of Neurotrauma*, 36(22), 3063. https://doi.org/10.1089/NEU.2018.6171

Hagler, D. J., Hatton, S. N., Cornejo, M. D., Makowski, C., Fair, D. A., Dick, A. S., Sutherland, M. T., Casey, B. J., Barch, D. M., Harms, M. P., Watts, R., Bjork, J. M., Garavan, H. P., Hilmer, L., Pung, C. J., Sicat, C. S., Kuperman, J., Bartsch, H., Xue, F., ... Dale, A. M. (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. *NeuroImage*, 202, 116091.
https://doi.org/10.1016/J.NEUROIMAGE.2019.116091
Ilyka, D., Johnson, M. H., & Lloyd-Fox, S. (2021). Infant social interactions and brain development: A systematic review. *Neuroscience and Biobehavioral Reviews*, 130, 448–469. https://doi.org/10.1016/J.NEUBIOREV.2021.09.001

Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. *Biostatistics*, 8(1), 118–127. https://doi.org/10.1093/BIOSTATISTICS/KXJ037

Kievit, R. A., Brandmaier, A. M., Ziegler, G., van Harmelen, A. L., de Mooij, S. M. M., Moutoussis, M., Goodyer, I. M., Bullmore, E., Jones, P. B., Fonagy, P., Lindenberger, U., & Dolan, R. J. (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. *Developmental Cognitive Neuroscience*, 33(October 2017), 99–117. https://doi.org/10.1016/j.dcn.2017.11.007

King, D. J., Ellis, K. R., Seri, S., & Wood, A. G. (2019). A systematic review of cross-sectional differences and longitudinal changes to the morphometry of the brain following paediatric traumatic brain injury. *NeuroImage: Clinical*, 23(April), 101844. https://doi.org/10.1016/j.nicl.2019.101844

Lalonde, G., Bernier, A., Beaudoin, C., Gravel, J., & Beauchamp, M. H. (2016). Investigating social functioning after early mild TBI: The quality of parent-child interactions. *Journal of Neuropsychology*. https://doi.org/10.1111/jnp.12104

Lalonde, G., Bernier, A., Beaudoin, C., Gravel, J., & Beauchamp, M. H. (2020). Factors contributing to parent–child interaction quality following mild traumatic brain injury in early childhood. *Journal of Neuropsychology*, 14(1), 98–120. https://doi.org/10.1111/JNP.12179

Levan, A., Baxter, L., Kirwan, C. B., Black, G., & Gale, S. D. (2015). Right frontal pole cortical thickness and social competence in children with chronic traumatic brain injury: Cognitive proficiency as a mediator. *Journal of Head Trauma Rehabilitation*, 30(2), E24–E31. https://doi.org/10.1097/HTR.0000000000000040

Li, L., & Liu, J. (2013). The effect of pediatric traumatic brain injury on behavioral outcomes: a systematic review. *Developmental Medicine and Child Neurology*, 55(1), 37. https://doi.org/10.1111/J.1469-8749.2012.04414.X

Mayer, A. R., Hanlon, F. M., & Ling, J. M. (2015). Gray Matter Abnormalities in Pediatric Mild Traumatic Brain Injury. *Journal of Neurotrauma*, 32(10), 723–730. https://doi.org/10.1089/neu.2014.3534

Mills, K. L., Lalonde, F., Clasen, L. S., Giedd, J. N., & Blakemore, S.-J. J. (2014). Developmental changes in the structure of the social brain in late childhood and adolescence. *Social Cognitive and Affective Neuroscience*, 9(1), 123–131. https://doi.org/10.1093/scan/nss113

Rosseel, Y. (2012). **lavaan**: An R Package for Structural Equation Modeling. *Journal of Statistical Software*, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

Ryan, N. P., Anderson, V. A., Bigler, E. D., Dennis, M., Taylor, H. G., Rubin, K. H., Vannatta, K., Gerhardt, C. A., Stancin, T., Beauchamp, M. H., Hearps, S., Catroppa, C., & Yeates, K. O. (2021). Delineating the Nature and Correlates of Social Dysfunction after Childhood Traumatic Brain Injury Using Common Data Elements: Evidence from an International Multi-Cohort Study. *Journal of Neurotrauma*, 38(2), 252. https://doi.org/10.1089/NEU.2020.7057

Ryan, N. P., Catroppa, C., Beare, R., Silk, T. J., Crossley, L., Beauchamp, M. H., Yeates, K. O., & Anderson, V. (2016). Theory of mind mediates the prospective relationship between abnormal social brain network morphology and chronic behavior problems after pediatric traumatic brain injury. *Social Cognitive and Affective Neuroscience*, 11(4), 683–692. https://doi.org/10.1093/scan/nsw007
Ryan, N. P., Catroppa, C., Godfrey, C., Noble-Haeusslein, L. J., Shultz, S. R., O'Brien, T. J., Anderson, V., & Semple, B. D. (2016). Social dysfunction after pediatric traumatic brain injury: a translational perspective HHS Public Access. *Neurosci Biobehav Rev, 64*, 196-214. https://doi.org/10.1016/j.neubiorev.2016.02.020

Ryan, N. P., van Bijnen, L., Catroppa, C., Beauchamp, M. H., Crossley, L., Hearps, S., & Anderson, V. (2016). Longitudinal outcome and recovery of social problems after pediatric traumatic brain injury (TBI): Contribution of brain insult and family environment. *International Journal of Developmental Neuroscience, 49*, 23-30. https://doi.org/10.1016/j.ijdevneu.2015.12.004

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. *Psychometrika* 66:4, 66(4), 507-514.
https://doi.org/10.1007/BF02296192

Tamnes, C. K., Herting, M. M., Goddings, A.-L., Meuwese, R., Blakemore, S.-J., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., Crone, E. A., & Mills, K. L. (2017). Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness. *The Journal of Neuroscience*, 37(12), 3402-3412.
https://doi.org/10.1523/JNEUROSCI.3302-16.2017

Tuerk, C., Anderson, V., Bernier, A., & Beauchamp, M. H. (2021). Social competence in early childhood: An empirical validation of the SOCIAL model. *Journal of Neuropsychology*, 15(3), 477-499. https://doi.org/10.1111/JNP.12230

Urban, K. J., Riggs, L., Wells, G. D., Keightley, M., Chen, J.-K. K., Ptito, A., Fait, P., Taha, T., & Sinopoli, K. J. (2017). Cortical Thickness Changes and Their Relationship to Dual-Task Performance following Mild Traumatic Brain Injury in Youth. *Journal of Neurotrauma*, 34(4), 816-823.
https://doi.org/10.1089/neu.2016.4502

Wilde, E. A., Merkley, T. L., Bigler, E. D., Max, J. E., Schmidt, A. T., Ayoub, K. W., McCauley, S. R., Hunter, J. v., Hanten, G., Li, X., Chu, Z. D., & Levin, H. S. (2012). Longitudinal changes in cortical thickness in children after traumatic brain injury and their relation to behavioral regulation and emotional control. *International Journal of Developmental Neuroscience*, 30(3), 267-276.
https://doi.org/10.1016/j.ijdevneu.2012.01.003

Zamani, A., Mychasiuk, R., & Semple, B. D. (2019). Determinants of social behavior deficits and recovery after pediatric traumatic brain injury. *Experimental Neurology*, 314, 34-45. https://doi.org/10.1016/J.EXPNEUROL.2019.01.007

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Bigler, E. D., Yeates, K. O., Dennis, M., Gerhardt, C. A., Rubin, K. H., Stancin, T., Gerry Taylor, H., & Vannatta, K. (2013). Neuroimaging and social behavior in children after traumatic brain injury: findings from the Social Outcomes of Brain Injury in Kids (SOBIK) study. *NeuroRehabilitation*, 32(4), 707–720. https://doi.org/10.3233/NRE-130896

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Bogner, J. A., Whiteneck, G. G., MacDonald, J., Juengst, S. B., Brown, A. W., Philippus, A. M., Marwitz, J. H., Lengenfelder, J., Mellick, D., Arenth, P., & Corrigan, J. D. (2017). Test-Retest Reliability of Traumatic Brain Injury Outcome Measures: A Traumatic Brain Injury Model Systems Study. *Journal of Head Trauma Rehabilitation*.
https://doi.org/10.1097/HTR.0000000000000291

Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., Garavan, H., Orr, C. A., Wager, T. D., Banich, M. T., Speer, N. K., Sutherland, M. T., Riedel, M. C., Dick, A. S., Bjork, J. M., Thomas, K. M., ... Dale, A. M. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. *Developmental Cognitive Neuroscience*, 32, 43–54. https://doi.org/10.1016/j.dcn.2018.03.001

Catroppa, C., Anderson, V., Beauchamp, M. H., & Yeates, K. O. (2015). *New frontiers in pediatric traumatic brain injury: an evidence base for clinical practice*. Routledge.

Corrigan, J. D., & Bogner, J. (2007). Initial reliability and validity of the Ohio state university TBI identification method. *Journal of Head Trauma Rehabilitation*, 22(6), 318–329. https://doi.org/10.1097/01.HTR.0000300227.67748.77

DeCasien, A. R., Guma, E., Liu, S., & Raznahan, A. (2022). Sex differences in the human brain: a roadmap for more careful analysis and interpretation of a biological reality. *Biology of Sex Differences* 2022 *13:1*, 13(1), 1–21. https://doi.org/10.1186/S13293-022-00448-W

Dégeilh, F., Bernier, A., Gravel, J., & Beauchamp, M. H. (2018). Developmental trajectories of adaptive functioning following early mild traumatic brain injury. *Developmental Psychobiology*, 60(8), 1037–1047. https://doi.org/10.1002/dev.21786
Dennis, E. L., Faskowitz, J., Rashid, F., Babikian, T., Mink, R., Babbitt, C., Johnson, J., Giza, C. C., Jahanshad, N., Thompson, P. M., & Asarnow, R. F. (2017). Diverging volumetric trajectories following pediatric traumatic brain injury. *NeuroImage: Clinical*, 15, 125-135. https://doi.org/10.1016/j.nicl.2017.03.014

Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. *NeuroImage*. https://doi.org/10.1016/j.neuroimage.2006.01.021

Farah, M. J. (2017). The Neuroscience of Socioeconomic Status: Correlates, Causes, and Consequences. *Neuron*, 96(1), 56–71. https://doi.org/10.1016/J.NEURON.2017.08.034

Fischl, B. (2012). FreeSurfer. *NeuroImage*, 62(2), 774–781.
https://doi.org/10.1016/J.NEUROIMAGE.2012.01.021

Frith, U., & Frith, C. D. (2003). Development and neurophysiology of mentalizing. *Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences*, 358(1431), 459–473. https://doi.org/10.1098/rstb.2002.1218

Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R. Z., Heeringa, S., Jernigan, T., Potter, A., Thompson, W., & Zahs, D. (2018). Recruiting the ABCD sample: Design considerations and procedures. *Developmental Cognitive Neuroscience*, 32, 16–22.
https://doi.org/10.1016/J.DCN.2018.04.004

Gerring, J. P., & Wade, S. (2012). The Essential Role of Psychosocial Risk and Protective Factors in Pediatric Traumatic Brain Injury Research. *Journal of Neurotrauma*, 29(4), 621. https://doi.org/10.1089/NEU.2011.2234

Gupte, R., Brooks, W., Vukas, R., Pierce, J., & Harris, J. (2019). Sex Differences in Traumatic Brain Injury: What We Know and What We Should Know. *Journal of Neurotrauma*, 36(22), 3063. https://doi.org/10.1089/NEU.2018.6171

Hagler, D. J., Hatton, S. N., Cornejo, M. D., Makowski, C., Fair, D. A., Dick, A. S., Sutherland, M. T., Casey, B. J., Barch, D. M., Harms, M. P., Watts, R., Bjork, J. M., Garavan, H. P., Hilmer, L., Pung, C. J., Sicat, C. S., Kuperman, J., Bartsch, H., Xue, F., ... Dale, A. M. (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. *NeuroImage*, 202, 116091.
https://doi.org/10.1016/J.NEUROIMAGE.2019.116091

Ilyka, D., Johnson, M. H., & Lloyd-Fox, S. (2021). Infant social interactions and brain development: A systematic review. *Neuroscience and Biobehavioral Reviews*, 130, 448–469. https://doi.org/10.1016/J.NEUBIOREV.2021.09.001

Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. *Biostatistics*, 8(1), 118–127.
https://doi.org/10.1093/BIOSTATISTICS/KXJ037

Kievit, R. A., Brandmaier, A. M., Ziegler, G., van Harmelen, A. L., de Mooij, S. M. M., Moutoussis, M., Goodyer, I. M., Bullmore, E., Jones, P. B., Fonagy, P., Lindenberger, U., & Dolan, R. J. (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. *Developmental Cognitive Neuroscience*, 33(October 2017), 99–117. https://doi.org/10.1016/j.dcn.2017.11.007

King, D. J., Ellis, K. R., Seri, S., & Wood, A. G. (2019). A systematic review of cross-sectional differences and longitudinal changes to the morphometry of the brain following paediatric traumatic brain injury. *NeuroImage: Clinical*, 23(April), 101844.
https://doi.org/10.1016/j.nicl.2019.101844

Lalonde, G., Bernier, A., Beaudoin, C., Gravel, J., & Beauchamp, M. H. (2016). Investigating social functioning after early mild TBI: The quality of parent-child interactions. *Journal of Neuropsychology*. https://doi.org/10.1111/jnp.12104
Lalonde, G., Bernier, A., Beaudoin, C., Gravel, J., & Beauchamp, M. H. (2020). Factors contributing to parent–child interaction quality following mild traumatic brain injury in early childhood. *Journal of Neuropsychology*, 14(1), 98–120.
https://doi.org/10.1111/JNP.12179

Levan, A., Baxter, L., Kirwan, C. B., Black, G., & Gale, S. D. (2015). Right frontal pole cortical thickness and social competence in children with chronic traumatic brain injury: Cognitive proficiency as a mediator. *Journal of Head Trauma Rehabilitation, 30*(2), E24–E31.
https://doi.org/10.1097/HTR.0000000000000040

Li, L., & Liu, J. (2013). The effect of pediatric traumatic brain injury on behavioral outcomes: a systematic review. *Developmental Medicine and Child Neurology, 55*(1), 37. https://doi.org/10.1111/J.1469-8749.2012.04414.X

Mayer, A. R., Hanlon, F. M., & Ling, J. M. (2015). Gray Matter Abnormalities in Pediatric Mild Traumatic Brain Injury. *Journal of Neurotrauma, 32*(10), 723–730.
https://doi.org/10.1089/neu.2014.3534

Mills, K. L., Lalonde, F., Clasen, L. S., Giedd, J. N., & Blakemore, S.-J. J. (2014). Developmental changes in the structure of the social brain in late childhood and adolescence. *Social Cognitive and Affective Neuroscience, 9*(1), 123–131.
https://doi.org/10.1093/scan/nss113

Rosseel, Y. (2012). **lavaan**: An R Package for Structural Equation Modeling. *Journal of Statistical Software, 48*(2), 1–36. https://doi.org/10.18637/jss.v048.i02

Ryan, N. P., Anderson, V. A., Bigler, E. D., Dennis, M., Taylor, H. G., Rubin, K. H., Vannatta, K., Gerhardt, C. A., Stancin, T., Beauchamp, M. H., Hearps, S., Catroppa, C., & Yeates, K. O. (2021). Delineating the Nature and Correlates of Social Dysfunction after Childhood Traumatic Brain Injury Using Common Data Elements: Evidence from an International Multi-Cohort Study. *Journal of Neurotrauma, 38*(2), 252.
https://doi.org/10.1089/NEU.2020.7057

Ryan, N. P., Catroppa, C., Beare, R., Silk, T. J., Crossley, L., Beauchamp, M. H., Yeates, K. O., & Anderson, V. (2016). Theory of mind mediates the prospective relationship between abnormal social brain network morphology and chronic behavior problems after pediatric traumatic brain injury. *Social Cognitive and Affective Neuroscience, 11*(4), 683–692. https://doi.org/10.1093/scan/nsw007

Ryan, N. P., Catroppa, C., Godfrey, C., Noble-Haeusslein, L. J., Shultz, S. R., O'Brien, T. J., Anderson, V., & Semple, B. D. (2016). Social dysfunction after pediatric traumatic brain injury: a translational perspective HHS Public Access. *Neurosci Biobehav Rev, 64*, 196–214. https://doi.org/10.1016/j.neubiorev.2016.02.020

Ryan, N. P., van Bijnen, L., Catroppa, C., Beauchamp, M. H., Crossley, L., Hearps, S., & Anderson, V. (2016). Longitudinal outcome and recovery of social problems after pediatric traumatic brain injury (TBI): Contribution of brain insult and family environment. *International Journal of Developmental Neuroscience, 49*, 23–30.
https://doi.org/10.1016/j.ijdevneu.2015.12.004

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. *Psychometrika* 66:4, 66(4), 507–514.
https://doi.org/10.1007/BF02296192

Tamnes, C. K., Herting, M. M., Goddings, A.-L., Meuwese, R., Blakemore, S.-J., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., Crone, E. A., & Mills, K. L. (2017). Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness. *The Journal of Neuroscience, 37*(12), 3402–3412.
https://doi.org/10.1523/JNEUROSCI.3302-16.2017
Tuerk, C., Anderson, V., Bernier, A., & Beauchamp, M. H. (2021). Social competence in early childhood: An empirical validation of the SOCIAL model. *Journal of Neuropsychology*, 15(3), 477–499. https://doi.org/10.1111/JNP.12230

Urban, K. J., Riggs, L., Wells, G. D., Keightley, M., Chen, J.-K. K., Ptito, A., Fait, P., Taha, T., & Sinopoli, K. J. (2017). Cortical Thickness Changes and Their Relationship to Dual-Task Performance following Mild Traumatic Brain Injury in Youth. *Journal of Neurotrauma*, 34(4), 816–823. https://doi.org/10.1089/neu.2016.4502

Wilde, E. A., Merkley, T. L., Bigler, E. D., Max, J. E., Schmidt, A. T., Ayoub, K. W., McCauley, S. R., Hunter, J. v., Hanten, G., Li, X., Chu, Z. D., & Levin, H. S. (2012). Longitudinal changes in cortical thickness in children after traumatic brain injury and their relation to behavioral regulation and emotional control. *International Journal of Developmental Neuroscience*, 30(3), 267–276.
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