Authors: Nicola L. de Souza (1Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA), Wenjing Meng (2Division of Biostatistics and Bioinformatics, School of Public Health, University of California San Diego, La Jolla, CA, USA), Florin Vaida (2Division of Biostatistics and Bioinformatics, School of Public Health, University of California San Diego, La Jolla, CA, USA), Joanna Jacobus (3Department of Psychiatry, University of California San Diego, La Jolla, CA, USA), Elisabeth A. Wilde (1Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA), Emily L. Dennis (1Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA), Erin D. Bigler (1Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA; 4Department of Psychology, Brigham Young University, Provo, UT, USA), Xia Yang (5Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA), Michael Cheng (5Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA), Emily A. Troyer (3Department of Psychiatry, University of California San Diego, La Jolla, CA, USA), Tracy Abildskov (1Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA), John R. Hesselink (6Department of Radiology, University of California San Diego, La Jolla, CA, USA), Jeffrey E. Max (3Department of Psychiatry, University of California San Diego, La Jolla, CA, USA)
Categories: Article, Pediatric traumatic brain injury, Concussion, Cognition, Longitudinal analysis, Pre-injury functioning, Control group comparison
Source: Journal of the International Neuropsychological Society : JINS
Authors: Nicola L. de Souza, Wenjing Meng, Florin Vaida, Joanna Jacobus, Elisabeth A. Wilde, Emily L. Dennis, Erin D. Bigler, Xia Yang, Michael Cheng, Emily A. Troyer, Tracy Abildskov, John R. Hesselink, Jeffrey E. Max
Most children recover from mild traumatic brain injury (mTBI), but some experience persistent neurocognitive effects. Understanding is limited due to methodological differences and a lack of pre-injury data. The study aimed to assess changes in neurocognitive outcomes in children following mTBI compared to orthopedic injury (OI) and non-injured (NI) controls, while accounting for pre-injury functioning.
Data were drawn from the Adolescent Brain and Cognitive Development (ABCD) study, a prospective longitudinal cohort. The sample included children with mTBI between the 1-year and 2-year follow-ups (n=83), identified by parent report of head injury with memory loss or loss of consciousness, compared to children who experienced OI within the same period (n=231) and an NI control group (n=218). Changes in neurocognitive outcomes from baseline to the 2-year follow-up between groups (mTBI vs. OI; mTBI vs. NI) were estimated using linear mixed-effects models, accounting for demographic, behavioral, genetic, and white matter microstructural covariates.
At baseline prior to injury, the mTBI group demonstrated better performance on picture vocabulary and crystallized composite scores than the OI group. At post-injury, after adjusting for pre-injury baseline differences, children who sustained an mTBI were no different in any measure of neurocognitive outcomes compared to OI and NI controls.
The findings highlight the importance of accounting for pre-injury differences when evaluating neurocognitive outcomes following pediatric mTBI. Neurocognitive differences within a year post-injury may be more related to pre-existing individual factors rather than the injury itself, underscoring the need for a comprehensive approach in studying pediatric mTBI.
Pediatric traumatic brain injury (TBI) is a major public health concern worldwide. Global incidence estimates range from 12 to 486 per 100,000 children annually, with mild TBI (mTBI) accounting for over 80% of cases in most regions (Dewan et al., 2016). In the United States (US), the annual number of children affected by mTBI is likely well over 1.5 million when including both emergency department (Centers for Disease Control and Prevention, 2019; Waltzman et al., 2020) and outpatient visits (Lumba-Brown et al., 2018; Mannix et al., 2013). These figures likely underestimate the true incidence of mTBI, as many children are not treated in these settings (Arbogast et al., 2016; Cassidy et al., 2004). Given the high incidence of mTBI during a critical developmental period, concerns have been raised about its impact on neurobehavioral and psychiatric outcomes (Max et al., 2021; H. G. Taylor et al., 2010), as well as neurocognitive function. However, findings on the neurocognitive effects of pediatric mTBI are inconsistent.
Some studies report reduced neurocognitive performance following mTBI, particularly in attention (Moore et al., 2016; Scherwath et al., 2011), episodic memory (Babikian et al., 2011; McCauley et al., 2014; Nance et al., 2009; Rieger et al., 2013; Thomas et al., 2011), and working memory (Kooper et al., 2024; Moore et al., 2016; Nance et al., 2009; Scherwath et al., 2011). Reduced performance has been observed in children with mTBI relative to control groups (Babikian et al., 2011; Chadwick et al., 2021; Kooper et al., 2024; McCauley et al., 2014; Moore et al., 2016; Rieger et al., 2013; Scherwath et al., 2011) and standardized norms (Nance et al., 2009; Scherwath et al., 2011; Thomas et al., 2011), across various time points post-injury, including within 1 month (Nance et al., 2009), within 3 months (Rieger et al., 2013; Scherwath et al., 2011; Thomas et al., 2011), 3–6 months (Chadwick et al., 2021), 6–12 months (Babikian et al., 2011), and beyond 12 months (Kooper et al., 2024; Moore et al., 2016) after injury. However, effect sizes tend to be small (Babikian & Asarnow, 2009; Goh et al., 2021; Hacker et al., 2023), raising questions about the clinical significance of these findings. Conversely, a greater number of studies report no persistent neurocognitive effects of mTBI (Babikian et al., 2015; Satz et al., 1997), regardless of whether outcomes were assessed using traditional neuropsychological tests (Anderson et al., 2005; Asarnow et al., 1995; Studer et al., 2014), computerized assessments (Brooks et al., 2013; Iverson et al., 2006; Jones et al., 2019; Maillard-Wermelinger et al., 2009), or general measures of cognition and academic achievement (Bijur et al., 1990, 1996; McKinlay et al., 2002), and whether outcomes were evaluated within the first month (Iverson et al., 2006), within 3–6 months (Studer et al., 2014), within 6–12 months (Asarnow et al., 1995; Maillard-Wermelinger et al., 2009), or beyond 12 months (Anderson et al., 2005; Bijur et al., 1990, 1996; Brooks et al., 2013; Jones et al., 2019) post-injury.
Several methodological limitations may contribute to these inconsistencies (Goh et al., 2021; Hacker et al., 2023; Satz et al., 1997). First, many studies lack clear injury characterization or vary in their inclusion of injury criteria, such as alteration of consciousness, loss of consciousness, or neuroimaging abnormalities (Yeates, 2010). Second, differences in sample characteristics, particularly heterogeneity in age at injury make it difficult to separate the injury-related effects from typical developmental changes (Lindsey et al., 2019). Third, few studies account for pre-injury neurocognitive function, and those that do focus on academic performance or rely on retrospective parent reports, which may introduce bias (Bijur et al., 1990; McKinlay et al., 2002).
Additionally, it is important to account for other factors that may influence neurocognitive outcomes following injury. Pre-morbid demographic and family (e.g., socioeconomic status, parental education) factors may interact with injury-related variables to influence neurocognitive outcomes and recovery following mTBI (Beauchamp et al., 2018; Kooper et al., 2023). Lower socioeconomic status, lower parental education, and cultural factors have been associated with prolonged recovery, persistent symptoms, and worse cognitive performance (Kennepohl et al., 2004; Rigney et al., 2023). There is also a need to consider factors beyond traditional demographic and family variables. Given that childhood is a period of rapid neuronal development, individual differences in brain maturation may influence recovery trajectories. Additionally, genetic factors associated with neuroinflammation, neural repair, and plasticity have been implicated in neurocognitive outcomes following mTBI (Kurowski et al., 2019, 2017; Treble-Barna et al., 2020), and population ancestry can modify risks associated with neurocognition (Naslavsky et al., 2022). While prior research has examined influences such as academic function and family environment, incorporating structural neuroimaging and genetic ancestry data offers a more nuanced understanding of injury response and recovery following injury.
The overall goal of the current study was to assess neurocognitive outcomes in children with mTBI. We leveraged data from the nationally representative Adolescent Brain and Cognitive Development (ABCD) study, a prospective, longitudinal study of children’s health and brain development. Participants included children who sustained an mTBI or orthopedic injury (OI) after study enrollment and those who were non-injured (NI). Comprehensive pre-injury data were collected at study baseline to characterize status prior to mTBI. Thus, the aim of the study was to examine change in multiple neurocognitive domains from pre-injury to post-injury in children with mTBI in comparison to children with OI and NI, while accounting for pre-morbid demographics, behavioral, genetic, and structural neuroimaging variables.
The ABCD Study (https://abcdstudy.org/) is a prospective and longitudinal study of children’s health and brain development. The study enrolled 11,868 children aged 9–10 from 2016 to 2018 across 21 sites in the US. Participants were followed annually for their biopsychosocial data, including demographic, genetic, behavioral, mental, neuroimaging, and neurocognitive assessments (Barch et al., 2018). For this study, ABCD baseline data and 2-year follow-up data (release 5.1) were used. Parental consent and child assent were obtained prior to participation and study procedures were approved by the University of California, San Diego Institutional Review Board to ensure research was conducted in accordance with the Helsinki Declaration.
Children’s brain injury history was assessed through a modified Ohio State Traumatic Brain Injury Screen-Short Form (Bogner et al., 2017; Corrigan & Bogner, 2007). Caregivers were asked nine yes/no questions about scenarios their children may have experienced that could lead to a concussion/TBI at their baseline visit and 2-year follow-up visit. TBI was categorized into five Improbable TBI (no TBI or TBI without LOC or memory loss); Possible mild TBI (TBI without LOC but memory loss); Mild TBI (TBI with LOC ≤ 30 min); Moderate TBI (TBI with LOC 30 min - 24 hours); Severe TBI (TBI with LOC ≥ 24 hours). Possible mild and mild TBI groups were combined to form the mTBI group for analysis. The classification of mTBI relied on the presence of memory loss when LOC was absent. As such, this criterion may have led to underestimation of mTBI, particularly in children who experienced other post-concussive symptoms but did not report or recall memory loss. Further, as acute clinical imaging data were not available, we could not determine how many cases were complicated versus uncomplicated mTBI. However, T1-weighted MRI scans acquired at the 2-year follow-up were reviewed by a board-certified neuroradiologist to assess for structural abnormalities and incidental findings.
Children’s OI history was assessed by the medical history questionnaire, where parents answered the question “Has she/he ever been to a doctor for broken bones”.
This study is part of an ongoing longitudinal design within the ABCD study, and participant groups were constructed to examine the effects of mTBI at each wave. The current analysis focused on injuries reported between the 1-year (Wave 1) and the 2-year (Wave 2) follow-ups (Figure 1), as the neuropsychological assessment occurs every two years, and we aimed to capture mTBI effects within a one-year window of that assessment. Future waves are planned, and similar group construction procedures will apply to those analyses. At each wave, injury groups are constructed based on newly reported injuries in the prior year. Participants who report an OI or mTBI and have not been previously included in any injury or comparison group are eligible for inclusion. Similarly, a random subset of eligible non-injured participants is selected to form the NI group, matched in size to the OI group. This strategy ensures sufficient sample sizes for each wave while preserving the broader pool of eligible NI participants for future waves.
Of the 11,868 participants enrolled at baseline, 461 were excluded due to prior mTBI (n = 450), moderate/severe TBI (n = 7), or missing injury data (n = 4), leaving 11,407 participants for subsequent categorization. Based on baseline injury history, 1,604 participants had an OI history, and 9,803 had NI history.
At Wave 1, injury status was reassessed. Within the baseline OI group, 165 participants reported new injuries in the prior OI (n = 140), mTBI (n = 23), or moderate/severe TBI (n = 2). Within the baseline NI group, 372 experienced new OI (n = 284), mTBI (n = 87), moderate/severe TBI (n = 1). Additionally, 424 NI participants were randomly selected for Wave 1 analyses. Participants with new OI or mTBI at Wave 1, or who were selected for prior analyses, were not eligible for Wave 2 analyses. Those with moderate/severe TBI were excluded.
At Wave 2, 546 participants were excluded due to missing injury data (n = 544) or moderate/severe TBI (n = 2). Between the 1-year and 2-year follow-ups, 98 participants sustained an mTBI and 264 had OI. To maintain balanced groups, 264 NI participants were randomly selected from the eligible cohort. Participants with missing covariate data were excluded, resulting in a final analytic sample of 231 OI, 218 NI, and 83 mTBI participants.
The neurocognitive variables of interest were selected from the ABCD 70-minute neurocognitive battery administered at baseline and 2-year follow-up, which comprised various cognitive tests covering key cognitive processes such as executive function, learning and memory, academic, and language (Luciana et al., 2018).
The battery consists of seven tests to assess language ability, processing speed, attention, episodic memory, working memory, and executive function. Five tasks were administered at both the baseline and 2-year visits; List Sorting Working Memory Test and Dimensional Change Card Sort Task were not assessed at the 2-year visit and were not included in analyses.
Our primary outcomes were the Crystallized and Fluid Cognition Composite scores. The Crystallized Cognition Composite score was calculated from the Picture Vocabulary and Oral Reading Tasks (Akshoomoff et al., 2013). Although the Fluid Cognition Composite score was intended as a co-primary outcome, the composite score could not be calculated due to missing data from the List Sort and Card Sort tasks.
Secondary outcomes included the individual component Picture Vocabulary, Oral Reading, Pattern Comparison Processing Speed Test, Picture Sequence Memory Test, and Flanker Task. These assessed specific domains such as language, reading ability, processing speed, visuospatial sequencing and memory, and cognitive control and attention, respectively. Uncorrected scores were included in analyses.
This task assesses children’s verbal learning and memory (Schmidt, 1996). The task requires participants to listen and recall a 15-item word list (List A), and this procedure was repeated four additional times using the same word list. After the five learning trials, participants listened and recalled a new set of words (List B). Immediately after recalling List B, participants were asked to recall List A again, and the number of corrected recalled words was the RAVLT short-delay score. After a 30-minute delay, participants were asked to recall List A again, and the number of correct responses was the RAVLT long-delay score (B. Wang et al., 2022). Both RAVLT short-delay and long-delay scores were treated as secondary outcomes.
Children’s age, sex assigned at birth, race, ethnicity, highest parental income, and highest parental education and whether the child had ever participated in a sport were reported by parents at baseline visits. We used ABCD’s range of income and education and both were treated as continuous variables. Income ranges from 1 to 10, where 1= Less than 5,000 through 12,000 through 16,000 through 25,000 through 35,000 through 50,000 through 75,000 through 100,000 through 200,000 and greater. Education ranges from 0 to 21, where 0 = Never attended/Kindergarten; 1 through 12 corresponds to 1st through 12th grade; 13 = High school graduate; 14 = GED or equivalent Diploma; 15 = Some college; 16 = Associate’s Occupational; 17 = Associate’s Academic Program; 18 = Bachelor’s degree; 19 = Master’s degree; 20 = Professional School degree; 21 = Doctoral degree.
To account for confounding from population stratification due to genetic ancestry in the multi-ancestry cohort, adjusted models incorporated the first ten genetic principal components (PCs) of the genotype data as covariates, computed by the ABCD consortium using methods from GENESIS R package (Fan et al., 2023).
The Behavioral Inhibition and Behavioral Approach Systems (BIS/BAS) and Impulsive Behavior Scale - Short Version (UPPS-P) were used to assess reward responsivity and trait impulsivity, respectively, at study baseline (Barch et al., 2021). We included four subscale scores from the BIS/BAS (BIS sum score, Reward Responsivity, Drive, Fun Seeking) and five subscale scores from the UPPS-P (Negative Urgency, Positive Urgency, Lack of Planning, Sensation Seeking, Lack of Perseverance) as covariates in analyses.
Diffusion magnetic resonance imaging scans were acquired across the research sites at study baseline using Siemens Prisma, GE750, and Philips 3T scanners. Scanning protocols were standardized across all sites and scanners. The full imaging acquisition protocol and ABCD processing pipeline have been described in detail elsewhere (Casey et al., 2018; Hagler et al., 2019). Briefly, the images were corrected for eddy current distortions, head motion, spatial and intensity distortions, and gradient nonlinearity distortions. Diffusion tensor imaging metrics, including fractional anisotropy (FA), mean diffusivity, radial diffusivity and axial diffusivity were calculated for major white matter tracts using the AtlasTrack atlas (Hagler et al., 2009). All raw and processed images were reviewed by trained technicians as part of quality control procedures (Hagler et al., 2019).
In the current study, FA values in the following tracts were included as covariates in adjusted models based on reported associations with mTBI (Lindsey et al., 2021): (1) right and left cingulum (cingulate portion), (2) right and left uncinate, (3) right and left inferior longitudinal fasciculus, (4) right and left inferior frontal-occipital fasciculus, (5) right and left superior longitudinal fasciculus, (6) right and left temporal superior longitudinal fasciculus (arcuate fasciculus) and (7) forceps minor.
For each neurocognitive outcome, the primary group comparison was between mTBI and OI with a secondary comparison between mTBI and NI groups, as pre-specified in the study design phase. The Crystallized Cognition Composite score was designated as the primary outcome and was assessed using an alpha level of 0.05. All secondary outcomes were assessed using the Holm method to control the family-wise error rate across the seven secondary outcomes.
Linear mixed-effects models were used to account for within-participant correlation of the baseline and 2-year follow-up scores. For each of the outcomes, model selection procedures were conducted. We began by determining whether a random intercept or random intercept and time slope model would provide a better fit. We first fitted a saturated model, i.e., including all age, sex, race, ethnicity, parental income, parental education, sport participation, 4 BIS/BAS subscale scores, 5 UPPS-P subscale scores, 10 genetic PCs, and 13 FA values as well as group (mTBI/NI/OI), time (baseline, 2-year follow-up), and their interaction. The restricted maximum likelihood (REML) Akaike Information Criterion (AIC) was used to choose between random intercept and random slope models. The saturated model with the chosen random effect structure was refitted using maximum likelihood (ML) estimation. Backward model selection with a threshold of p < 0.20 was applied to the potential confounders (Vittinghoff et al., 2012). Unadjusted analyses used similar modeling strategies but without confounders included in the model.
Table 1 summarizes participant characteristics for each group. As expected from the study design, the groups were similar in age, with a mean (standard deviation) of 9.93 (0.62) years. They also showed comparable distributions in ethnicity and prior sports participation, with most participants identifying as non-Hispanic (81%) and having played at least one sport (89%). The mTBI group was no different from the OI group in terms of sex, race, and parental income. However, compared to the NI group, the mTBI group had a higher proportion of males (mTBI: 66% vs. NI: 41%, p < .001), more participants identifying as multiracial (mTBI: 20% vs. NI: 8.7%, p = .004), and higher parental income (mTBI = 7.46 (1.65) vs. NI = 6.59 (2.48), p < .001). Additionally, the mTBI group had higher parental education levels than both the OI and NI groups (mTBI = 18.45 (1.51) vs. NI = 17.07 (2.59), p < .001; mTBI vs. OI = 17.59 (2.45), p < .001).
At study baseline prior to injury, the mTBI group had lower BIS sum scores (mTBI = 8.82 (3.17) vs. NI = 9.82 (3.67), p = .02), indicating lower inhibition and sensitivity to negative outcomes, and higher UPPS-P sensation seeking scores (mTBI = 10.48 (2.59) vs. NI = 9.70 (2.75), p = .02) than the NI group, but were no different from the OI group (ps > .05). There were no significant differences among groups in reward responsivity, drive, fun seeking, positive urgency, negative urgency, lack of planning, or lack of perseverance scores (ps > .05).
The mTBI group had significantly higher genetic PC1 values compared to the NI group (mTBI = 0.37 (0.60) vs. NI = 0.07 (1.03), p = .002), but did not differ from the OI group (ps > .05). No other baseline differences were found in genetic PCs (ps > .05).
Baseline comparisons of white matter microstructure showed elevated FA in the left cingulate, right and left superior longitudinal fasciculus, and left temporal superior longitudinal fasciculus in the mTBI group relative to the NI group, but no differences compared to the OI group. Of the 83 children with mTBI, 73 completed imaging that was reviewed by a board-certified neuroradiologist. At the 2-year follow-up, 13 children showed non-normal findings. In 10 of these cases, the abnormalities were already present at baseline and therefore not attributable to a subsequent injury. The remaining 3 children (4.1%) had new or additional findings at follow-up, but none were characteristic of trauma-related injury.
Table 2 summarizes the neurocognitive outcomes at study baseline (pre-injury) and 2-year follow-up (post-injury) for each group. In fully adjusted models, there were significant pre-injury group differences at study baseline between mTBI and OI in Crystallized Composite scores (coefficient = −1.58, 95% CI =−3.02, −0.14, p = .031), where the mTBI had higher scores than the OI group (Table 3; Supplemental Material for full tables). However, there were no significant Group x Time interaction for the Crystallized Composite, indicating a lack of mTBI effect post-injury on the primary outcome.
Among secondary outcomes in fully adjusted models, there were neither pre-injury group differences nor Group x Time interactions for any of the seven secondary measures after correction for multiple comparisons (Table 3; Supplemental Material for full tables).
Age-corrected scores for NIH toolbox outcome measures were also examined and reported in the Supplemental Material, with findings consistent with the reported analyses using uncorrected scores.
Despite the high incidence of pediatric mTBI, controversies remain regarding the neurobehavioral outcomes associated with mTBI. Methodological limitations and variations in study design have made it challenging to isolate the direct effects of injury from pre-existing individual and developmental factors. The current study builds on previous work by incorporating comprehensive pre-injury neurocognitive, demographic, behavioral, genetic, and structural neuroimaging data within a nationally representative sample that was not selected based on injury status. Post-injury neurocognitive outcomes in children who sustained an mTBI during the course of the study were also compared to those who sustained OI or remained non-injured. Our findings revealed no significant differences in neurocognitive outcomes between children with mTBI relative to either control group.
These results are consistent with prior studies that found no persisting neurocognitive effects following pediatric mTBI (Anderson et al., 2005; Asarnow et al., 1995; Babikian et al., 2015, 2011; Jones et al., 2019; Maillard-Wermelinger et al., 2009; Studer et al., 2014). Our findings also align with retrospective analyses of baseline ABCD data, where children with a history of mTBI showed no differences from OI and NI controls in Flanker performance (Betz et al., 2024) and where neurocognitive performance did not predict odds of prior mTBI (Dufour et al., 2020).
A key strength of this study is the inclusion of pre-injury neurocognitive measures, allowing for an objective assessment of cognitive outcomes following pediatric mTBI. Importantly, this approach controlled for pre-existing neurocognitive differences, which we observed between the mTBI and OI groups with the mTBI group performing better on the crystallized cognition composite measure. Previous prospective longitudinal birth cohort studies, such as the British Birth Cohort Study (Bijur et al., 1990) and the Christchurch Health and Development Longitudinal Study (McKinlay et al., 2002), included pre-injury data primarily focused on academic achievement measures, including reading, mathematics, and vocabulary, when evaluating cognitive outcomes following mTBI. These studies similarly found no significant reductions in cognition between children with mild head injury and both non-injured (Bijur et al., 1990; McKinlay et al., 2002) and OI control groups (Bijur et al., 1990). The inclusion of multiple objective neurocognitive tests in the current study expands on these previous studies by assessing both crystallized and fluid cognition, capturing a broader range of cognitive abilities. Further, the use of the same standardized neurocognitive measures before and after injury allows for a post-injury assessment of cognitive change, and the two-year interval between tests reduces the likelihood that changes are due to practice effects, which would also apply to the control group.
In addition to pre-injury neurocognitive measures, a strength of this study is the inclusion of a wide range of contextual factors, including demographic variables, genetic data, and neuroimaging. This addresses a major limitation of prior individual studies, which may lack sufficient samples to model such complexity. Importantly, having access to these data allows us to control for factors that may independently contribute to differences in neurocognitive performance across groups, beyond the injury itself.
Another strength of this study was the inclusion of multiple control groups. At baseline, children who later sustained mTBI were no different to the OI group but differed from the NI group, particularly in characteristics associated with general injury risk. The mTBI group had a higher proportion of males, lower BIS sum scores indicating lower sensitivity to punishment, and higher sensation seeking scores compared to the NI group, all of which are factors associated with injury predisposition (Schwebel & Gaines, 2007). Further, baseline differences in genetic and white matter measures were found between the mTBI and NI groups but not between the mTBI and OI groups. Another study also found differences in DTI measures of white matter structure between mTBI and uninjured controls but not between mTBI and OI controls (Wilde et al., 2019). Our results align with prior studies suggesting that cognitive differences between both mTBI and OI groups relative to NI controls reflect a general injury effect rather than one specific to mTBI (Babikian et al., 2011; McCauley et al., 2014). Thus, individual pre-existing factors including behavioral traits, genetics, and brain structure may contribute to the likelihood of sustaining injuries in general. Further, these findings suggest that studies lacking control groups (Nance et al., 2009; Thomas et al., 2011) or comparing only to an NI group (Kooper et al., 2024; Moore et al., 2016; Scherwath et al., 2011) may overestimate the effects specific to mTBI. Taken together, this study adds further evidence that these differences may stem from pre-existing individual factors rather than the direct effects of mTBI, reinforcing the need for multiple control groups in future research.
Many previous studies have focused on children medically diagnosed with mTBI in the ED (Babikian et al., 2011; Nance et al., 2009; Scherwath et al., 2011; Studer et al., 2014), which may overrepresent the more severe spectrum of mild injury. In contrast, the current study includes a broader range of mTBI severity, capturing children who may not have received medical care or a formal diagnosis. This approach allows for the inclusion of milder injuries, such as those involving transient alterations in consciousness but no LOC, as well as injuries with LOC of up to 30 minutes, consistent with established mTBI definitions (Department of Veterans Affairs/Department of Defense, 2016; Kay et al., 1993). Notably, a higher proportion of children included in the mTBI group did not experience LOC, aligning with previous estimates suggesting that many pediatric mTBIs do not result in LOC (Lee et al., 2014; M. Y. Wang et al., 2000) and may be unreported or managed outside of the ED (Arbogast et al., 2016; Macpherson et al., 2014; A. M. Taylor et al., 2015). By including these milder injuries, our findings may be more representative of mTBI cases than studies that include only hospital-based samples.
Despite its strengths, this study had several limitations. The neurocognitive assessments available in the ABCD study, while validated for use in children (Akshoomoff et al., 2013), were not specifically designed for brain injury populations and may lack the sensitivity to detect subtle effects of mTBI. As such, our findings may reflect limitations in assessment rather than a true absence of differences. The NIH Toolbox Cognitive Battery, though useful as a broad assessment of neurocognition, was not specifically developed for mTBI. Further, the Fluid Cognition composite score could not be computed due to missing measures at follow-up, but may be the domain expected to be most affected by mTBI (Babikian & Asarnow, 2009; Chadwick et al., 2021; Tulsky et al., 2017). Similarly, the RAVLT, used to assess verbal learning, has demonstrated poor psychometric properties for studying mild TBI (Bigler, 2024; Bigler et al., 2024) and shows minimal changes even across different levels of injury (Kennedy et al., 2024). Alternative approaches may be necessary to detect subtle neurocognitive effects. For instance, dual-tasking paradigms, where a cognitive task is performed while balancing or walking, have revealed neurocognitive impairments that are not evident when tasks are performed in isolation (Wilkerson et al., 2021). Moreover, neurocognitive testing may not fully capture cognitive symptoms that impact daily functioning, as individuals with mTBI may remain symptomatic despite normal neurocognitive performance (Heitger et al., 2009), highlighting the importance of including broader symptom assessments beyond objective neurocognitive evaluations.
Injury data were obtained through a structured caregiver report, which may be subject to recall bias. However, this limitation is mitigated given that injuries occurred within the past year. The study sample was restricted to children who sustained their first recognized mTBI, as those with prior reported injuries were excluded. However, it is possible that parents did not recall more remote injuries. While this method captures injuries on the milder end of the injury spectrum, there was no medical documentation or acute imaging of injury, limiting the ability to determine occurrences of complicated mTBI. Relatedly, injuries were characterized by presence of LOC or memory-related symptoms, potentially excluding children who experienced other symptoms such as nausea or headache. However, given the mild nature of the injuries in this sample, this exclusion is unlikely to substantially alter findings. More broadly, the term “mTBI” is often inconsistently defined in clinical and research contexts, particularly in children, where it often overlaps with the term “concussion”. Although our criteria were based on established definitions, they do not resolve this conceptual ambiguity, and diagnostic precision remains limited. This should be considered when interpreting the generalizability and specificity of our findings. Finally, although the study design ensured that injuries occurred within the past year, the exact timing of each injury was not collected. The study also did not collect the mechanism of injury.
This study incorporated comprehensive pre-injury data and multiple control groups, finding no evidence of persistent neurocognitive declines within a year of pediatric mTBI. The findings suggest that previously reported cognitive differences may stem from pre-existing factors rather than mTBI itself. Our results did not support the idea of a general injury or a unique mTBI effect, highlighting the importance of rigorous study design when evaluating injury outcomes. Future research should continue to refine methodological approaches to better isolate the effects of injury from pre-existing individual differences.