Predictors of longitudinal outcome and recovery of pragmatic language and its relation to externalizing behaviour after pediatric traumatic brain injury

Brain & Language 142 (2015) 86–95

Contents lists available at ScienceDirect

Brain & Language journal homepage: www.elsevier.com/locate/b&l

Predictors of longitudinal outcome and recovery of pragmatic language and its relation to externalizing behaviour after pediatric traumatic brain injury Nicholas P. Ryan a,c,⇑, Cathy Catroppa a,b,c,f,1, Richard Beare d,2, Lee Coleman e,3, Michael Ditchfield a,4, Louise Crossley a,1, Miriam H. Beauchamp g,h,5, Vicki A. Anderson a,b,c,f,6 a

Australian Centre for Child Neuropsychological Studies, Murdoch Childrens Research Institute, Melbourne, Australia Department of Psychology, Royal Children’s Hospital, Melbourne, Australia c Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia d Developmental Imaging, Murdoch Childrens Research Institute, Melbourne, Australia e Department of Radiology, Royal Children’s Hospital, Melbourne, Australia f Department of Pediatrics, University of Melbourne, Melbourne, Australia g Department of Psychology, University of Montreal, Montreal, Canada h Ste-Justine Research Center, Montreal, Quebec, Canada b

a r t i c l e

i n f o

Article history: Accepted 5 January 2015 Available online 9 February 2015 Keywords: Child Brain injuries Magnetic resonance imaging Pragmatic language Neurobiology

a b s t r a c t The purpose of the present investigation was to evaluate the contribution of age-at-insult and brain pathology on longitudinal outcome and recovery of pragmatic language in a sample of children and adolescents with traumatic brain injury (TBI). Children and adolescents with mild to severe TBI (n = 112) were categorized according to timing of brain insult: (i) Middle Childhood (5–9 years; n = 41); (ii) Late Childhood (10–11 years; n = 39); and (iii) Adolescence (12–15 years; n = 32) and group-matched for age, gender and socio-economic status (SES) to a typically developing (TD) control group (n = 43). Participants underwent magnetic resonance imaging (MRI) including a susceptibility weighted imaging (SWI) sequence 2–8 weeks after injury and were assessed on measures of pragmatic language and behavioural functioning at 6- and 24-months after injury. Children and adolescents with TBI of all severity levels demonstrated impairments in these domains at 6-months injury before returning to age-expected levels at 2years post-TBI. However, while adolescent TBI was associated with post-acute disruption to skills that preceded recovery to age-expected levels by 2-years post injury, the middle childhood TBI group demonstrated impairments at 6-months post-injury that were maintained at 2-year follow up. Reduced pragmatic communication was associated with frontal, temporal and corpus callosum lesions, as well as more frequent externalizing behaviour at 24-months post injury. Findings show that persisting pragmatic language impairment after pediatric TBI is related to younger age at brain insult, as well as microhemorrhagic pathology in brain regions that contribute to the anatomically distributed social brain network. Relationships between reduced pragmatic communication and more frequent externalizing behavior underscore the need for context-sensitive rehabilitation programs that aim to increase interpersonal effectiveness and reduce risk for maladaptive behavior trajectories into the long-term post injury. Ó 2015 Elsevier Inc. All rights reserved.

⇑ Corresponding author at: Child Neuropsychology, c/o Murdoch Children’s Research Institute, Flemington Road, Parkville, 3052, Australia. Fax: +61 3 9345 5544. E-mail addresses: [email protected] (N.P. Ryan), [email protected] (C. Catroppa), [email protected] (R. Beare), [email protected] (L. Coleman), michael.ditchfi[email protected] (M. Ditchfield), [email protected] (L. Crossley), [email protected] (M.H. Beauchamp), vicki. [email protected] (V.A. Anderson). 1 Address: Child Neuropsychology, c/o Murdoch Childrens Research Institute, Flemington Road, Parkville, 3052, Australia. 2 Address: Developmental Imaging, c/o Murdoch Childrens Research Institute, Flemington Road, Parkville, Australia. 3 Address: Department of Medical Imaging, c/o Royal Children’s Hospital, Flemington Road, Parkville, 3052, Australia. 4 Address: Monash Medical Centre, Clayton Road, Clayton, Australia. 5 Address: Department of Psychology, University of Montreal, 6128 Succursale Centre-Ville, Montreal, Quebec H3C 3J7, Canada. 6 Address: Department of Psychology, Royal Children’s Hospital, Flemington Road, Parkville, 3052, Australia. Fax: + 61 3 9345 5544. http://dx.doi.org/10.1016/j.bandl.2015.01.007 0093-934X/Ó 2015 Elsevier Inc. All rights reserved.

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95

1. Introduction Childhood TBI is a common cause of childhood disability, and is associated with elevated risk for cognitive, social and behavioural impairment (Beauchamp & Anderson, 2010; Rosema, Crowe, & Anderson, 2012; Yeates et al., 2007). While evidence suggests that some children with TBI experience difficulties with basic aspects of expressive and receptive language (Chapman, Levin, Wanek, Weyrauch, & Kufera, 1998; Ewing-Cobbs et al., 1997; Sullivan & Riccio, 2010), such injuries can also be associated with long-term impairments in higher order aspects of pragmatic communication (Didus, Anderson, & Catroppa, 1999; Ryan et al., 2013; Sullivan & Riccio, 2010), defined as the ability to use and comprehend language in context (Watts & Douglas, 2006). Preliminary reports show that these impairments have profound consequences for social and behavioural functioning among individuals with TBI (Ryan et al., 2013; Yeates et al., 2004), but factors contributing to variability in outcome and recovery of pragmatic communication across individual children remain poorly understood. Pragmatic communication emerges rapidly during middle childhood (Dennis & Barnes, 1990; Gerrard-Morris et al., 2010), mediated by a distributed network of brain regions implicated in a range of specific executive and social cognitive processes, including the anterior temporal lobes, orbitofrontal cortex, lateral frontopolar cortex, anterior prefrontal cortex and the inferior and superior parietal lobe (Barbey, Colom, & Grafman, 2013). Pragmatic communication impairments suggest that brain regions involved in these anatomically distributed neural networks may be vulnerable to the effects of TBI (Chapman et al., 2004; Didus et al., 1999; Ryan et al., 2013), however the challenge remains to identify factors that confer elevated risk for poor long-term outcome and recovery of these skills. The Heuristic Model of Social Competence (Yeates et al., 2007) provides a framework for conceptualizing how injury-related, child, and environmental factors may explain variability in social outcome across individual survivors of childhood TBI. More specifically, the model postulates that various injury and noninjury-related risk and resilience factors may independently or interactively contribute to social outcome after childhood TBI. Injury factors, including injury severity, lesion location and timing of cerebral insult, are conceptualized as risk factors that increase the likelihood of impaired social information processing and communication. Environmental factors such as interventions and better family functioning represent sources of resilience that may buffer against the neurological consequences of injury. Moreover, in keeping with diathesis-stress perspectives (Yeates et al., 2007), the model accounts for the possibility that impairments in one or more aspects of neurocognitive functioning (social cognitive, cognitive–executive) may influence social and behavioural functioning either directly or indirectly via their influence on social interaction. For example, it may be that social communicative dysfunction associated with injury leads to rejection by peers and subsequent distress, reflected in externalizing behaviour symptoms (e.g. aggression, rule breaking, conduct problems) that persist or even worsen with time since injury (Alderman, 2003; Cattelani, Lombardi, Brianti, & Mazzucchi, 1998; Li & Liu, 2013; Ylvisaker, Turkstra, & Coelho, 2005). While there is preliminary evidence for a dose–response relationship between injury severity and social outcomes (Catroppa & Anderson, 2004; Rosema et al., 2012; Ryan, Anderson, et al., 2014), damage to one or multiple components of anatomically distributed social cognitive neural networks is likely to disrupt acquisition of high-level social functions, including pragmatic communication (Kennedy & Adolphs, 2012). Based on lesion

87

studies that link impairments in social functioning to damage to particular areas of the frontal and temporal cortices (Geraci, Surian, Ferraro, & Cantagallo, 2010; Muller et al., 2010), it may be that focal lesions to these brain regions contribute to impaired pragmatic language. Another approach may be to target the corpus callosum (CC) as an index of white matter disruption, and a common site of injury in pediatric TBI (Beauchamp, Ditchfield, Catroppa, et al., 2011; Levin et al., 2000). Since the CC grows and establishes white matter connecting pathways between high level association cortices implicated in a range of specific social cognitive and executive processes (Ewing-Cobbs et al., 2012), it is plausible that damage to this structure in childhood may disrupt structural connectivity and thus interfere with the acquisition and establishment of pragmatic language skills. Age at brain insult may influence outcome and recovery of neurobehavioural skills (Anderson, Spencer-Smith, & Wood, 2011; Anderson et al., 2009; Jacobs, Harvey, & Anderson, 2007), although this is likely not a linear association, but rather influenced by critical periods during development, such that outcomes are dependent on neurological and cognitive development at the time of insult (Anderson et al., 2009, 2011; Crowe, Catroppa, Babl, Rosenfeld, & Anderson, 2012; Dennis, 1988; Dennis et al., 2014; Kolb, Pellis, & Robinson, 2004). It has been argued that neurocognitive skills emerging or developing at the time of insult are at heightened risk for persisting disruption, while established skills may experience transient disruption before recovering to levels that approximate pre-injury baseline (Dennis, 1988; Dennis et al., 2014). Since pragmatic communication skills undergo protracted development through the early school years and into adolescence (Didus et al., 1999; Dumontheil, Apperly, & Blakemore, 2010; Gerrard-Morris et al., 2010), a critical period model would predict that these skills are at heightened risk for disruption during middle childhood, when they are undergoing rapid development and refinement. Although there is preliminary evidence that poorer pragmatic language is associated with younger age at brain insult (Chapman et al., 1998; Didus et al., 1999), the limited size and age range of previous samples underscores the need for further research to evaluate the contribution of timing of cerebral insult to longitudinal outcome and recovery of these skills. The purpose of the present prospective longitudinal study was to investigate (1) the contribution of age-at-insult to outcome and recovery of pragmatic communication after TBI sustained in middle childhood (5–9 years), late childhood (10–11 years) and early adolescence (12–15 years); (2) examine relations between pragmatic communication, injury severity and brain pathology; and (3) evaluate relationships between pragmatic communication and externalizing symptoms at 6- and 24-months post-TBI. In keeping with critical period perspectives (Anderson et al., 2009, 2011; Crowe et al., 2012; Dennis, 1988; Dennis et al., 2014; Kolb et al., 2004), we expected that relative to typically developing (TD) controls, children sustaining TBI in middle childhood would show impaired pragmatic language at 6- and 24-months post injury. Conversely, TBI in late childhood and adolescence would be associated with significantly reduced performance at 6-months post-injury, but comparable performance to TD controls at 24 months, reflecting recovery of pre-injury function. We also predicted that poorer pragmatic communication would be associated with (i) greater injury severity, (ii) frontal and temporal brain pathology, and (iii) white matter pathology (i.e. corpus callosum lesions). Consistent with diathesis-stress perspectives (Yeates et al., 2007), we predicted that poorer pragmatic communication would be associated with more frequent externalizing symptoms, as measured by aggression, conduct problems, and rule breaking at 6- and 24-months post injury.

88

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95

2. Method 2.1. Sample This prospective longitudinal study comprised 112 children and adolescents with TBI and 43 typically developing (TD) children and adolescents, group-matched for age, gender and socio-economic status (SES). The present study reports data from the 6- and 24month post-injury assessments. All participants were ascertained between 2007 and 2010, and were aged between 5.3 and 15.4 years at time of recruitment. Children and adolescents with TBI were recruited at time of injury, and represented consecutive admissions to the Royal Children’s Hospital (RCH), Melbourne, Australia. For the TBI group, inclusion criteria were: (i) age 5.0–15.4 years at recruitment; (ii) documented evidence of a closed head injury; (iii) medical records sufficiently detailed to determine injury severity; (iv) no documented history of pre-injury neurological or developmental disorder, non-accidental injury or previous TBI; (v) no prior intervention for social impairment; (vi) English speaking. The TD group were required to meet inclusion criteria (i), (iv) and (v) and (vi) above. Participants with TBI were classified as: (i) mild TBI (n = 58): Glasgow Coma Score (GCS) 13–15, no evidence of mass lesion on CT or clinical MRI; (ii) mild complicated TBI (n = 13): GCS 13–15, evidence of mass lesion on CT or clinical MRI; (iii) moderate TBI (n = 22): GCS 9–12, and/or mass lesion or other evidence of specific injury on CT/MRI, and/or neurological impairment; (iv) severe TBI (n = 13): GCS 3–8, and/or mass lesion or other evidence of specific injury on CT/MRI, and/or neurological impairment. TBI participants were further categorized into age-at-injury subgroups. The rationale for these groups was based on timing of cerebral growth spurts (Giza & Prins, 2006; Kolb et al., 2004; Van Praag, Kempermann, & Gage, 2000) and has previously been used in pediatric TBI research (Anderson et al., 2009; Crowe et al., 2012). The sample was divided into three groups, which were well matched for injury severity: Middle Childhood (n = 41), 5–9 years at injury; Late Childhood (n = 39), 10–11 years at injury; and Adolescence (n = 32), 12–15 years at injury. Typically developing (TD) control children were similarly divided into three groups: Middle Childhood (n = 18), 5–9 years; Late Childhood (n = 14), 10– 11 years; and Adolescence (n = 11), 12–15 years. The study was approved by the RCH Human Research Ethics Committee. All parents gave their written, informed consent for children to participate in the study, and for extraction of clinical data from medical records at the time of recruitment.

2.2. Measures 2.2.1. Pragmatic language Making Inferences from the Test of Language CompetenceExpanded Edition (TLC-E; Wigg & Secord, 1989) evaluates the ability to make permissible inferences on the basis of existing causal relationships or chains on short paragraphs. The task requires the ability to interpret propositions, recognize and generate underlying social scripts, and to make logical inferences based on knowledge of possible causal chains in an evoked script. Ageadjusted standard scores were calculated and employed for statistical analyses (M = 10; SD = 3). In keeping with the approach adopted in previous studies (Anderson et al., 2013; Farina, Albanese, & Pons, 2007; Hallett, 1997; Taylor et al., 2014), this test was designed to measure language pragmatics, as distinct from other domains of metalinguistic competence assessed using the TLC-E, such as language semantics and syntax (Dennis & Barnes, 1990).

2.2.2. Externalizing behaviour The Child Behaviour Checklist (Achenbach & Rescorla, 2001) was completed based on child behaviour over the previous 6months. The respondent rates each of the 113 items as 0 = not true; 1 = somewhat or sometimes true; 2 = very true or often true), and provides an Internalizing, Externalizing, and Total Behaviour Problem Composite Score (T = 50; SD = 10). The Aggression, Conduct Problems, and Rule-Breaking subscales of the Externalizing Behaviour Composite (T = 50; SD = 10) were employed in the statistical analyses. 2.2.3. Neuropsychological examination All patients completed the two-subtest version of the Wechsler Abbreviated Intelligence Scale (WASI: Wechsler, 1999) to assess reasoning and to obtain an estimate of general intellectual functioning. Digit Span (DS: Wechsler, 2003) and Walk Don’t Walk from the Test of Everyday Attention for Children (TEA-Ch; Manly, Robertson, Anderson, & Nimmo-Smith, 1999) were administered to assess high-level attention and response inhibition, respectively. 2.3. Susceptibility weighted imaging 2.3.1. Image acquisition Children underwent a structural MRI research scan between 2 and 8 weeks post injury (M = 39.25, SD = 27.64 days). MR images were acquired on a 3 Tesla Siemens Trio scanner (Siemens Medical Systems, Erlangen, Germany) using a 32-Channel matrix head coil. Conventional MR sequences were performed using a standardized imaging protocol (Beauchamp, Ditchfield, Babl, et al., 2011). A SWI sequence was also acquired as a 3D slab-selective transverse volume (TR = 28 ms; TE = 20 ms; slice thickness = 1.5 mm; flip angle 15 deg; flow compensation, BW = 120 Hz/px, GRAPPA PAT2, magnitude/phase reconstruction, resolution 0.9  0.6  1.5 mm, mean number of slices 72). SWI is a relatively new technique that accentuates the magnetic properties of blood products, thereby rendering it useful for detecting small amounts of altered blood and blood product in the brain (Haacke, Xu, Cheng, & Reichenbach, 2004; Reichenbach, Venkatesan, Schillinger, Kido, & Haacke, 1997; Sehgal et al., 2005). SWI is shown to be more sensitive to microhaemorrhagic lesions commonly associated with traumatic axonal injury than more traditional techniques such as computed tomography and conventional magnetic resonance imaging sequences (Babikian et al., 2005; Beauchamp, Ditchfield, Babl, et al., 2011; Haacke, Mittal, Wu, Neelavalli, & Cheng, 2009; Spitz et al., 2013), and represents a useful biomarker for prediction of post-acute social and intellectual outcomes after pediatric TBI (Babikian et al., 2005; Beauchamp et al., 2013; Colbert et al., 2010; Ryan et al., 2015; Tong et al., 2004). SWI imaging is a variant of the standard 3D FLASH sequence that exploits the signal loss from shortened T2⁄ characteristics of calcium- and deoxyhemoglobin-containing lesions. The images are T2⁄ weighted because of the range of acceptable TEs used in the acquisition (18–22 ms). The increased sensitivity to shortened T2⁄ lesions is caused by the image reconstruction techniques used. Both magnitude and phase images are reconstructed from the data set. The phase images display a higher sensitivity to local susceptibility variations and, as such, are used as an image mask to be combined with the magnitude data set. The combined data set is then reconstructed using a sliding window (eight individual slices compressed into one image) minimum intensity projection (MIP) data set. The total acquisition time for the MRI protocol was 31:53 min. 2.3.2. SWI analyses SWI images were visually reviewed to determine scan quality. One scan was rejected due to poor quality. The neuroanatomical

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95

location of brain lesions was identified based on visual inspection of SWI scans by a pediatric neuroradiologist and neuropsychologist blind to patients’ clinical details. Lesions were identified and coded according to location (frontal, extra-frontal, subcortical) using a modification of the Coffey classification system (Beauchamp, Ditchfield, Babl, et al., 2011; Coffey & Figiel, 1991), which assessed the signal abnormality as seen on SWI images. Specifically signal changes identified on SWI scans were coded in grey and white matter in the following cortical and subcortical regions: frontal/ temporal/parietal/occipital lobes, cerebellum, hippocampus, amygdala, corpus callosum, thalamus, and basal ganglia. Scans rated positive for lesions on SWI were further investigated by manual segmentation using ITK-snap (Yushkevich et al., 2006). Lesion counts were conducted using a connected component analysis of lesion masks, which accounts for the possibility that multiple lesions may be present in any single independent region of the brain. Repeatability of segmentation was checked by re-segmenting 5 scans after a delay of greater than 6 months and comparing volumes using intra-class correlation (ICC). Lesion load provided an index of the extent of TBI-related neuropathology. Consistent with the approach employed by Kraus et al. (2007), lesion load was calculated as the total number of grey and white matter brain regions that showed signal abnormality as seen on SWI. This measure was employed because it is thought to be sensitive to diffuse abnormalities given that it considers the actual number of affected areas across the brain independent of individual variability in the specific location of these abnormalities (Kraus et al., 2007). 2.4. Statistical analysis All data were entered into SPSS statistical software (Version 21.0; SPSS, Inc., Chicago, IL) and screened for violations of normality. An alpha level of p < 0.05 was used to indicate significance, and effect sizes were calculated using Cohen’s d. Effect sizes below 0.2 were considered small, those between 0.2 and 0.5 as medium, and those above 0.5, as large (Cohen, 1988). Effect sizes greater than 0.67 were considered likely to be of clinical significance (Tabachnick & Fidell, 2001). Analysis of variance (ANOVA) or Chi-square test-for-independence was conducted to investigate group differences for demographic and clinical variables. Independent-Samples t-tests were employed to compare each age-at-injury subgroup with one of three independent age-matched control groups. ANCOVAs were employed in instances where the TBI and control groups were not matched on measures of cognitive function. With 80% power and an alpha-level of 0.05, ANCOVA was powered to detect a medium-to-large effect size (eta2 = 0.10). Pearson partial correlations (2-tailed) were used to investigate relationships between pragmatic communication outcomes and the extent and neuroanatomical location (frontal, temporal, parietal, corpus callosum) of SWI lesions, covarying for age at assessment and socio-economic status (SES) (Australian and New Zealand Standard Classification of Occupations (ANZSCO), McMillan, Beavis, & Jones, 2009). 3. Results 3.1. Sample characteristics Injury severity groups did not differ on age at injury, or age at 6or 24-month assessment. Groups differed on gender, v2 = 13.07, p < .001, and controls showed higher SES than the moderate TBI group, p = .02. SES was therefore used as a covariate in analyses of outcome. As expected, all injury severity groups differed on several injury-related variables including GCS (p < .001), length of intubation (p = .04), length of hospital stay (p < .001), and loss of

89

consciousness (p = .003). Groups did not differ on number of neurological symptoms. Table 1 presents characteristics for the age-at-insult and typically developing (TD) control subgroups. Groups did not differ on SES or gender. A significant age difference was identified between the adolescent TBI and TD control subgroups at 6-months postinjury, t = 2.20; p = .04, and thus age at assessment was included as a covariate in analyses of outcome for the adolescent TBI group. No other significant demographic differences were identified, p > .05. Adolescents with TBI were more likely to have required surgical intervention, v2 = 61.90, p < .001, and cause of injury also varied with age, with younger children more likely to suffer falls, v2 = 22.29, p < .001. There were no significant differences for injury variables: GCS, neurological signs, length of hospital stay or loss of consciousness (hours) (Table 1). No significant differences were identified between the Middle and Late Childhood TBI groups and their respective TD control groups on measures of IQ, high-level attention or inhibitory function (Table 1). Conversely, compared to the typically developing adolescents, the adolescent TBI group demonstrated significantly lower IQ and high-level attention at 6-months post-injury. The adolescent TBI group also demonstrated poorer inhibitory function at 24-months post-injury. Therefore, these variables were employed as covariates in analyses of outcome in the adolescent TBI group. 3.2. Child TBI and neuropathology Haemorrhagic lesions were detected in 37 patients (35%) across all severity groups (Beauchamp, Ditchfield, Babl, et al., 2011). No SWI lesions were detected in the TD group. Lesion number showed substantial variability (min 1, max 77). Segmentation procedures were reliable, with an intra-rater ICC score of .987 (95% Confidence Interval = .911–.999). Age-at-injury groups did not differ for the number of SWI lesions detected, F(2, 103) = .55; p = .58. Distribution and location of SWI lesions is represented in Fig. 1. 3.3. Pediatric TBI and pragmatic language 3.3.1. Middle childhood TBI At 6-months post-TBI, this group showed significantly poorer pragmatic communication than TD controls, t = 2.98; p = .004; Cohen’s d = .85. Differences between the TBI and TD control groups remained statistically significant at 24-months post-injury, t = 2.50; p = .016; d = .72 (Fig. 2). 3.3.2. Late childhood TBI Children with TBI showed comparable pragmatic language performance to TD controls at 6-months (t = .62; p = .538; d = .18) and 24-months post-injury (t = .51; p = .611; d = .17). 3.3.3. Adolescent TBI At 6-months post-injury, the TBI group showed significantly poorer pragmatic communication than TD controls, t = 2.03; p = .050; d = .69. The difference between the groups was no longer statistically significant when analyses were repeated covarying for age at assessment, working memory and WASI-2 IQ, F(1, 41) = .97; p = .33. The difference between the TBI and control group was not statistically significant at 24-months post-injury, t = .52; p = .611; d = .21. 3.4. Impact of injury severity After covarying for SES, ANCOVA revealed a significant main effect of injury severity on pragmatic communication at 6-months

90

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95

Table 1 Participant demographic and injury characteristics, and performance on measurements of IQ and executive function.

Demographics Total, n Male, n (%) SES, M (SD)c Age at injury, M (SD)b Age at 6-month assessment, M (SD)a,b Age at 24-month assessment, M (SD)b Injury characteristics Lowest GCS, M (SD) Neurological signs, M (SD) Length hospital stay (days), M (SD) Surgical intervention, n (%)b LOC (hours), M (SD)

Middle childhood (5–9 years)

Late childhood (10–11 years)

Adolescence (12–15 years)

Control

TBI

Control

TBI

Control

TBI

18 10 (56.0) 69.51 (20.98) – 7.38 (1.04) 8.94 (1.08)

41 24 (58.5) 60.57 (26.77) 7.50 (1.17) 8.05 (1.18) 9.48 (1.22)

14 10 (71.4) 75.15 (16.96) – 11.85 (1.33) 13.30 (1.36)

39 29 (74.4) 65.53 (23.41) 10.91 (.57) 11.47 (.59) 13.02 (.63)

11 4 (36.3) 74.18 (15.27) – 12.93 (2.90) 14.04 (3.03)

32 23 (71.9) 66.83 (23.48) 13.19 (.83) 13.72 (.87) 15.24 (.89)

– –

12.39 (3.51) 1.27 (.63) 3.65 (5.86) 4 (9.76) 3.20 (2.93)

– –

13.00 (2.96) 1.36 (.67) 3.76 (7.64) 5 (12.82) 3.69 (3.49)

– –

12.25 (3.65) 1.41 (.56) 4.76 (10.28) 6 (18.80) 3.53 (3.04)

–

Cause of injuryb MVA (car), n (%) MVA (pedestrian/bike), n (%) Fall (stationary), n (%) Fall (moving), n (%) Kicked/struck by object, n (%) Cognitive measures WASI-2 IQ, 6-months, M (SD)d WASI-2 IQ, 24-months, M (SD) Digit Span 6-months, M (SD)d Digit Span 24-months, M (SD) Walk Don’t Walk 6-months, M (SD) Walk Don’t Walk 24-months, M (SD)d a b c d

–

2 (5) 5 (12) 16 (39) 12 (29) 6 (15) 106.95 (14.67) 111.68 (14.87) 10.26 (2.86) 10.05 (2.44) 7.74 (3.59) 9.68 (4.50)

99.24 (14.77) 104.21 (12.36) 10.34 (2.83) 10.28 (3.32) 6.89 (3.20) 7.83 (3.86)

–

4 (10) 6 (15) 6 (15) 13 (33) 10 (26) 105.00 (15.14) 104.67 (11.55) 10.17 (2.79) 10.92 (3.09) 7.58 (4.74) 7.83 (3.04)

96.86 (12.26) 98.12 (13.72) 9.11 (3.26) 9.18 (3.57) 8.35 (3.90) 8.67 (3.36)

5 (16) 3 (9) 5 (16) 14 (44) 5 (16) 106.75 (10.05) 104.89 (8.16) 11.17 (2.29) 10.67 (3.54) 7.50 (3.40) 11.33 (3.91)

96.79 (14.73) 96.73 (15.19) 8.69 (2.42) 9.00 (2.29) 7.28 (3.44) 7.95 (3.17)

Significant difference between adolescent TBI vs. age-matched TD group. Significant difference between age-at insult subgroups. SES is based on the ANZSCO. The scale ranges from 0 to 100 with high scores reflecting higher occupation status for the primary caregiver. Significant difference between adolescent TBI vs. age-matched TD group.

post-injury, F(3, 146) = 4.55; p = .005. Tukey’s post hoc tests revealed that the severe TBI group was significantly less accurate than the control group, M difference [diff] = 2.38; p = .006. The mild (M diff = 1.09; p = .043) and moderate TBI group (diff = 2.14; p = .002) also demonstrated significantly worse performance than the TD control group. ANCOVA revealed no significant effect of injury severity at 24-months post-injury, F(3, 122) = 1.27; p = .287. However, the difference between the severe TBI group and TD controls was approaching statistical significance, M diff = 1.77; p = .058. 3.5. Relations between pragmatic communication and neuropathology Table 2 reports Pearson partial correlations (r) between pragmatic communication and pathology location, covarying for age at assessment and SES. For 6 month-outcomes, poorer pragmatic communication was significantly associated with the presence of lesions to the corpus callosum (r = .20; p = .043). No other significant relationships were identified (Table 2). For 24-month outcomes, reduced pragmatic communication was associated with the presence of frontal (r = .28; p = .01) and temporal brain pathology (r = .23; p = .043). No other significant relationships were identified (p > .05). 3.6. Relations between pragmatic communication and externalizing behaviour Although pragmatic communication was not significantly related to externalizing behaviour symptoms at 6-months postinjury (Table 3), pragmatic communication at 6-months post injury was negatively associated with parent-reported rule breaking behaviour at 24-months post injury, such that reduced pragmatic communication was associated with more frequent rule-breaking behaviour.

Moreover, pragmatic communication at 24-months post-injury was negatively correlated with externalizing behaviour symptoms at 24-months, such that poorer pragmatic communication was associated with more frequent aggression (r = .24; p = .036), rule-breaking (r = .33; p = .003) and conduct problems (r = .26; p = .023).

4. Discussion The purpose of the present longitudinal prospective study was to investigate the contribution of age-at-insult to outcome and recovery of pragmatic communication skills at 6- and 24-months post injury, and examine relations between outcome, injury severity and location of cerebral pathology detected using susceptibility-weighted imaging (SWI) sequences acquired at 2–8 weeks post-injury. A secondary aim was to investigate longitudinal relations between pragmatic communication and externalizing behaviour up to 2-years post injury. Results provided partial support for all expectations. Children and adolescents with TBI of all severity levels demonstrated impairments at 6-months post-injury before recovering to a level comparable to TD controls by 2-years post-TBI. In line with the critical period model (Anderson et al., 2009, 2011; Crowe et al., 2012; Dennis, 1989; Dennis et al., 2014; Kolb et al., 2004), results demonstrated that while adolescent TBI was associated with a post-acute disruption to skills that preceded recovery to ageexpected levels by 2-years post injury, the middle childhood TBI group demonstrated impairments at 6-months injury that were maintained at 2-year follow up. Moreover, in keeping with expectations, poorer pragmatic language was associated with more frequent externalizing behaviour at 6- and 24-months post-TBI, and these relationships were shown to strengthen with time since injury.

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95

91

Fig. 1. Probability distribution of brain lesions detected using SWI in the left lateral (A), left medial (B), right lateral (C) and right medial (D) hemispheres. Lesion distributions were created by aligning the individual T1 images to the Montreal Neurological Institute template using the non-linear normalization procedure in Statistical Parametric Mapping 8 (SPM8). The lesion maps were normalized using the same transformations. The aligned lesion masks were averaged to produce a single image illustrating the distribution of lesions in the study population. Hotter colors indicate the co-location of lesions in multiple subjects. Lesions were most prominent in frontal regions [frontal only = 15 patients, frontal + extrafrontal only = 6, frontal + other regions (CC = 1, deep gray + CC = 1, cerebellum = 1, cerebellum + CC = 1)], followed by extrafrontal regions only (N = 6). A small number of patients (4) had lesions in several areas (frontal + extrafrontal + cerebellum = 2, frontal + extrafrontal + deep gray = 1, frontal + extrafrontal + CC = 1). Very few patients had lesions solely in the CC (1), cerebellum (1) or deep gray (0) regions.

Fig. 2. Mean scores (and SD) on TLC-E Making Inferences as a function of timing of cerebral insult.

92

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95

Table 2 Pearson Partial correlations between pragmatic communication and pathology location. SWI variable

TLC 6-months

TLC 24-months

Pathology location Frontal Temporal Parietal Corpus callosum

.05 (.62) .12 (.24) .03 (.78) .20 (.04)

.28 (.01) .23 (.04) .07 (.52) .01 (.97)

.09 (.37)

.21 (.07)

Extent Total lesion load

TLC = Test of Language Competence. Lesion load = total number of independent brain regions affected. Bold: Significant correlation covarying for age at assessment and SES, p < .05, 2tailed.

Table 3 Pearson correlations (r) between pragmatic communication and externalizing behaviour (EB) in the TBI sample. Behaviour outcome

TLC 6-months

6-month Aggression Conduct problems Rule breaking Externalizing composite

.14 .14 .13 .15

(.16) (.17) (.19) (.14)

24-month Aggression Conduct problems Rule breaking Externalizing composite

.17 .18 .24 .21

(.13) (.11) (.03) (.06)

TLC 24-months – – – – .24 .26 .33 .29

(.04) (.02) (.00) (.01)

Bold: Significant correlation p < .05, 2-tailed. TLC = Test of Language Competence.

Although younger age at insult has traditionally been associated with elevated risk for neural damage and associated cognitive impairment (Anderson & Moore, 1995; Donders & Warschausky, 2007; Hebb, 1942), our findings suggest that outcome and recovery of pragmatic communication may be dependent on neurological and cognitive development at the time of injury (Anderson et al., 2009; Dennis et al., 2014). While evidence shows that basic aspects of social communication such as joint attention expand rapidly across the first years of life (Bates et al., 1997; Ewing-Cobbs et al., 2012), there is recent support for a protracted maturation of pragmatic communication through the early school years (Dumontheil et al., 2010; Gerrard-Morris et al., 2010), corresponding to extended structural and functional development of medial prefrontal and lateral temporo-parietal brain regions critically involved in these functions (Blakemore, 2008; Choudhury, Blakemore, & Charman, 2006; Giedd et al., 1999; Shaw et al., 2008). Our findings suggest that traumatic axonal injury during this critical period of cognitive and brain development may compromise structural connectivity between anatomically distributed brain regions, leading these high level skills to deviate from the normative developmental blueprint. In line with expectations, results demonstrated a dose– response relationship between injury severity and pragmatic communication at 6-months post-injury, such that severe TBI was associated with significantly poorer outcome relative to both TD controls and children with milder injuries. Perhaps surprisingly, relative to TD controls children with mild TBI showed reduced pragmatic communication skills at 6-months post-TBI. Although social dysfunction is documented in individuals with moderate– severe TBI (Rosema et al., 2012) we provide evidence that pragmatic language skills may also be vulnerable to the effects of milder generalized injuries.

In keeping with the findings of a dose–response relationship between injury severity and pragmatic communication at 6 but not 24-months post-injury, corpus callosum lesions were associated with outcome at 6-months post-TBI only. The corpus callosum is a common site of injury in pediatric TBI, and damage to this region has been shown to be associated with more diffuse injury (Ewing-Cobbs et al., 2012), poorer long term social cognitive outcomes (Ryan, Anderson, et al., 2014) and poorer parent ratings of social competence (Beauchamp et al., 2009) in samples of children sustaining TBI during the infant and preschool years. Though our findings are broadly consistent with previous research, it is perhaps surprising that neither injury severity or corpus callosum lesions were predictive of pragmatic language outcomes at 24-months post-injury. While this null finding may be partly attributable to the fact that relatively few participants had CC lesions identifiable on SWI, we speculate that this unexpected result may to some extent reflect the influence of timing of cerebral insult in the present TBI sample. Most interestingly, our results contrast with a recent study that linked corpus callosum injury in infancy and early childhood to very-long-term social cognitive impairment in young adulthood (Ryan, Anderson, et al., 2014). The discrepant findings of these studies may suggest that there is greater likelihood of persisting social dysfunction when brain injuries are sustained during developmental stages that coincide with rapid functional and structural maturation of CC fibres (Beauchamp et al., 2009; Giedd et al., 1996; Luders, Thompson, & Toga, 2010). More broadly, our findings suggest that compared to younger children who brains show relatively limited functional specificity and have few established skills at the time of insult (Dennis, 1989; Dennis et al., 2014), older children and adolescents may possess an established set of high-level skills that appear less vulnerable the long term consequences of diffuse cerebral insult. The relationship between pragmatic language and lesions to frontal and temporal brain regions is in keeping with evidence that inferential processes involved in pragmatic language engage an anatomically distributed network of brain regions that support construction of coherent mental models that integrate incoming language with prior social knowledge and experience (Badre & Wagner, 2006; Barbey et al., 2012, 2013; Gläscher et al., 2010). Pragmatic language functions are shown to recruit the anterior temporal lobes involved in the storage of social knowledge required for contextual understanding of others’ social interactions (Ferstl, Neumann, Bogler, & Von Cramon, 2008; Saxe, 2006; Stowe et al., 1998), as well as regions of a fronto-parietal network that is commonly engaged in tasks that require executive control processes. Although our results converge with prior research and lend support to the contribution of frontal and temporal brain regions to pragmatic language in children and adolescents, these findings must be considered in the developmental context that is unique to early brain insult. Since the development of high-level cognitive processes is mediated by activity-dependent interregional interactions between regions of large scale, anatomically distributed networks (Johnson et al., 2005), our findings suggest that insult to frontal and temporal brain regions during critical periods of brain development may compromise structural connectivity, and lead to downstream consequences for the development and acquisition of high level pragmatic communication skills. Our findings strengthen an emerging body of evidence that pragmatic-communicative impairments contribute to real-world social behavioural functioning, including externalizing behaviour problems that are shown to persist or even worsen with time since pediatric brain injury (Li & Liu, 2013; Muscara, Catroppa, & Anderson, 2008; Ryan et al., 2013). In keeping with expectations, while there was limited evidence for relationships between pragmatic communication and externalizing behaviour at 6-months post-injury, more frequent aggression and rule breaking at

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95

24-months post injury was associated with poorer pragmatic competence at 6- and 24-months post-injury. The relative strengthening of these relations with time since injury is consistent with evidence that awareness and insight into injury-related cognitive and communicative dysfunction may increase with time since injury (Hart, Seignourel, & Sherer, 2009). We suggest that relationships between pragmatic language and externalizing behaviour are best accounted for by a diathesisstress model. On the basis of our findings it may be that a reduced ability to infer meaning from everyday discourse reduces interpersonal effectiveness, and leads to alienation or rejection by interactive partners. Failure to successfully negotiate normative developmental social goals is likely to elicit distress and feelings of reduced self-efficacy, reflected in rule breaking and aggression that likely further reduce the individual’s capacity to maintain meaningful relationships with peers. Moreover, these findings converge with studies of other paediatric brain disorders including attention deficit hyperactivity disorder and autism (Factor, Rosen, & Reyes, 2013; Hattier, Matson, Belva, & Horovitz, 2011) to suggest that maladaptive behaviour trajectories in these conditions may represent the cumulative consequence of a ‘‘developmental cascade’’ (Masten & Cicchetti, 2010), whereby challenging behaviours may emerge through dynamic interplay between atypical neurodevelopmental processes, impaired neurocognitive function and disrupted and/or inconsistent environmental contingencies. 4.1. Clinical implications Our findings converge with previous reports of pragmatic communicative dysfunction in children and adolescents with TBI (Barnes & Dennis, 2001; Dennis & Barnes, 1990; Didus et al., 1999). In keeping with the HMSC model (Yeates et al., 2007), poorer pragmatic communication after pediatric TBI is linked to a range of clinically relevant injury-related risk factors, including injury severity, timing of cerebral insult, and the neuroanatomical location of cerebral pathology (Anderson et al., 2009; Dennis, 1989; Dennis et al., 2014). Of particular clinical relevance, while TBI of all injury severity levels was associated with disruption to pragmatic communication skills at 6-months post injury, results demonstrate that long term outcome and recovery of pragmatic communication may be dependent on neurological and cognitive development at the time of brain injury. More specifically, adolescents with TBI illustrated a recovery pattern consistent with ‘‘developmental lag’’ (Satz, Fletcher, Clark, & Morris, 1981), whereby this group showed impairments relative to age-matched controls at 6-months postinjury, before recovering to levels that approximated normative benchmarks by 24-months post TBI. In contrast, for the middle childhood TBI group in whom pragmatic language skills were likely undergoing rapid development at the time of insult (Dennis & Barnes, 1990), persisting observable impairments were maintained across time. Moreover, relationships between pragmatic language and externalizing behaviour suggest that children and adolescents in our sample may benefit from cognitive–communicative remediation programs designed to improve interpersonal effectiveness and reduce risk for maladaptive externalizing trajectories characterized by aggression, rule-breaking and intrusive behaviour. 4.2. Limitations and future directions Some methodological limitations weakened the strength of our findings. Firstly, due to colinearity between measures of injury severity and pre-injury family environment, we were unable to evaluate the respective contribution of socio-economic status and the quality of the family environment to pragmatic communication

93

outcomes. Further research is needed to determine whether environmental factors may interact with the effects of injury to moderate outcome and recovery of pragmatic communication. Moreover, since there is evidence that pragmatic communication shares cognitive and neural mechanisms with systems involved in working memory and social information processing (Barbey et al., 2013), we cannot rule out the possibility that differences on measures of pragmatic language are at least partially attributable to impairments in executive processes. However, since deficits were particularly pronounced for the TBI group matched to TD controls on measures of IQ, working memory and inhibitory functioning, differences between groups more likely reflects the primary pragmatic language impairment. Furthermore, while our findings suggest that adolescents with TBI recover pragmatic language to levels that approximate normative benchmarks by 24-months post-TBI, these results do not preclude the possibility that impairments persist for this group in other high-level cognitive functions that continue to illustrate protracted maturation into mid-to-late adolescence (Blakemore, 2008). The challenge thus remains to characterize the influence of age at insult on outcome and recovery of these high-level cognitive and affective behaviours (e.g. complex theory of mind, emotion regulation) that continue to illustrate extended development in parallel with structural and functional maturation of medial prefrontal and lateral temporo-parietal brain regions that are commonly vulnerable to disruption from paediatric TBI (Blakemore, 2008; Choudhury et al., 2006; Tasker, 2006).

5. Conclusions The findings of the present study converge to suggest that outcome and recovery of pragmatic communication is dependent on cognitive and neurological development at the time of cerebral insult. While older children and adolescents appear to recover to a level of function comparable to age-matched typically developing controls by 24-months post injury, our study provides evidence for long-term, persisting pragmatic-communicative dysfunction among children sustaining brain injuries during a period that may coincide with a critical stage of cognitive and neurological development. Moreover, the contribution of pragmatic language to externalizing behaviour was found to strengthen with time since injury, suggesting that younger children with TBI may benefit from cognitive–communicative remediation programs designed to improve interpersonal effectiveness, and thus reduce risk for maladaptive behavioural trajectories. References Achenbach, T. M., & Rescorla, L. (2001). ASEBA school-age forms & profiles. Burlington: Aseba. Alderman, N. (2003). Contemporary approaches to the management of irritability and aggression following traumatic brain injury. Neuropsychological Rehabilitation, 13(1–2), 211–240. Anderson, V., Beauchamp, M. H., Yeates, K. O., Crossley, L., Hearps, S. J., & Catroppa, C. (2013). Social competence at 6 months following childhood traumatic brain injury. Journal of the International Neuropsychological Society, 19(05), 539–550. Anderson, V., & Moore, C. (1995). Age at injury as a predictor of outcome following pediatric head injury: A longitudinal perspective. Child Neuropsychology, 1(3), 187–202. Anderson, V., Spencer-Smith, M., Leventer, R., Coleman, L., Anderson, P., Williams, J., et al. (2009). Childhood brain insult: Can age at insult help us predict outcome? Brain, 132(1), 45–56. Anderson, V., Spencer-Smith, M., & Wood, A. (2011). Do children really recover better? Neurobehavioural plasticity after early brain insult. Brain. awr103. Babikian, T., Freier, M. C., Tong, K. A., Nickerson, J. P., Wall, C. J., Holshouser, B. A., et al. (2005). Susceptibility weighted imaging: Neuropsychologic outcome and pediatric head injury. Pediatric Neurology, 33(3), 184–194. Badre, D., & Wagner, A. D. (2006). Computational and neurobiological mechanisms underlying cognitive flexibility. Proceedings of the National Academy of Sciences, 103(18), 7186–7191.

94

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95

Barbey, A. K., Colom, R., & Grafman, J. (2013). Neural mechanisms of discourse comprehension: A human lesion study. Brain. awt312. Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain. aws021. Barnes, M. A., & Dennis, M. (2001). Knowledge-based inferencing after childhood head injury. Brain and Language, 76(3), 253–265. Bates, E., Thal, D., Trauner, D., Fenson, J., Aram, D., Eisele, J., et al. (1997). From first words to grammar in children with focal brain injury. Developmental Neuropsychology, 13(3), 275–343. Beauchamp, M. H., & Anderson, V. (2010). SOCIAL: An integrative framework for the development of social skills. Psychological Bulletin, 136(1), 39. Beauchamp, M. H., Anderson, V. A., Catroppa, C., Maller, J. J., Godfrey, C., Rosenfeld, J. V., et al. (2009). Implications of reduced callosal area for social skills after severe traumatic brain injury in children. Journal of Neurotrauma, 26(10), 1645–1654. Beauchamp, M. H., Beare, R., Ditchfield, M., Coleman, L., Babl, F. E., Kean, M., et al. (2013). Susceptibility weighted imaging and its relationship to outcome after pediatric traumatic brain injury. Cortex, 49(2), 591–598. Beauchamp, M. H., Ditchfield, M., Babl, F. E., Kean, M., Catroppa, C., Yeates, K. O., et al. (2011). Detecting traumatic brain lesions in children: CT versus MRI versus susceptibility weighted imaging (SWI). Journal of Neurotrauma, 28(6), 915–927. Beauchamp, M. H., Ditchfield, M., Catroppa, C., Kean, M., Godfrey, C., Rosenfeld, J. V., et al. (2011). Focal thinning of the posterior corpus callosum: Normal variant or post-traumatic? Brain Injury, 25(10), 950–957. Blakemore, S.-J. (2008). The social brain in adolescence. Nature Reviews Neuroscience, 9(4), 267–277. Catroppa, C., & Anderson, V. (2004). Recovery and predictors of language skills two years following pediatric traumatic brain injury. Brain and Language, 88(1), 68–78. Cattelani, R., Lombardi, F., Brianti, R., & Mazzucchi, A. (1998). Traumatic brain injury in childhood: Intellectual, behavioural and social outcome into adulthood. Brain Injury, 12(4), 283–296. Chapman, S. B., Levin, H. S., Wanek, A., Weyrauch, J., & Kufera, J. (1998). Discourse after closed head injury in young children. Brain and Language, 61(3), 420–449. Chapman, S. B., Sparks, G., Levin, H. S., Dennis, M., Roncadin, C., Zhang, L., et al. (2004). Discourse macrolevel processing after severe pediatric traumatic brain injury. Developmental Neuropsychology, 25(1–2), 37–60. Choudhury, S., Blakemore, S.-J., & Charman, T. (2006). Social cognitive development during adolescence. Social Cognitive and Affective Neuroscience, 1(3), 165–174. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. San Diego: Academic Press. Coffey, C. E., & Figiel, G. S. (1991). Neuropsychiatric significance of subcortical encephalomalacia. In B. J. Carrol & J. E. Barrett (Eds.), Psychopathology and the Brain (pp. 243–264). New York: Raven Press. Colbert, C. A., Holshouser, B. A., Aaen, G. S., Sheridan, C., Oyoyo, U., Kido, D., et al. (2010). Value of cerebral microhemorrhages detected with susceptibilityweighted mr imaging for prediction of long-term outcome in children with nonaccidental trauma 1. Radiology, 256(3), 898–905. Crowe, L. M., Catroppa, C., Babl, F. E., Rosenfeld, J. V., & Anderson, V. (2012). Timing of traumatic brain injury in childhood and intellectual outcome. Journal of Pediatric Psychology, 37(7), 745–754. Dennis, M. (1988). Language and the young damaged brain. Dennis, M. (1989). Language and the young damaged brain. In T. Boll & B. K. Bryant (Eds.), Clinical neuropsychology and brain function: research, measurement and practice (pp. 85–124). Washington: American Psychological Association. Dennis, M., & Barnes, M. A. (1990). Knowing the meaning, getting the point, bridging the gap, and carrying the message: Aspects of discourse following closed head injury in childhood and adolescence. Brain and Language, 39(3), 428–446. Dennis, M., Spiegler, B. J., Simic, N., Sinopoli, K. J., Wilkinson, A., Yeates, K. O., et al. (2014). Functional plasticity in childhood brain disorders: When, what, how, and whom to assess. Neuropsychology Review, 1–20. Didus, E., Anderson, V. A., & Catroppa, C. (1999). The development of pragmatic communication skills in head injured children. Developmental Neurorehabilitation, 3(4), 177–186. Donders, J., & Warschausky, S. (2007). Neurobehavioral outcomes after early versus late childhood traumatic brain injury. The Journal of Head Trauma Rehabilitation, 22(5), 296–302. Dumontheil, I., Apperly, I. A., & Blakemore, S. J. (2010). Online usage of theory of mind continues to develop in late adolescence. Developmental Science, 13(2), 331–338. Ewing-Cobbs, L., Fletcher, J. M., Levin, H. S., Francis, D. J., Davidson, K., & Miner, M. E. (1997). Longitudinal neuropsychological outcome in infants and preschoolers with traumatic brain injury. Journal of the International Neuropsychological Society, 3(06), 581–591. Ewing-Cobbs, L., Prasad, M. R., Swank, P., Kramer, L., Mendez, D., Treble, A., et al. (2012). Social communication in young children with traumatic brain injury: Relations with corpus callosum morphometry. International Journal of Developmental Neuroscience, 30(3), 247–254. Factor, P. I., Rosen, P. J., & Reyes, R. A. (2013). The relation of poor emotional awareness and externalizing behavior among children with ADHD. Journal of Attention Disorders. http://dx.doi.org/10.1177/1087054713494005.

Farina, E., Albanese, O., & Pons, F. (2007). Making inferences and individual differences in emotion understanding. Psychology of Language and Communication, 11(2), 3–19. Ferstl, E. C., Neumann, J., Bogler, C., & Von Cramon, D. Y. (2008). The extended language network: A meta-analysis of neuroimaging studies on text comprehension. Human Brain Mapping, 29(5), 581–593. Geraci, A., Surian, L., Ferraro, M., & Cantagallo, A. (2010). Theory of mind in patients with ventromedial or dorsolateral prefrontal lesions following traumatic brain injury. Brain Injury, 24(7–8), 978–987. Gerrard-Morris, A., Taylor, H. G., Yeates, K. O., Walz, N. C., Stancin, T., Minich, N., et al. (2010). Cognitive development after traumatic brain injury in young children. Journal of the International Neuropsychological Society, 16(01), 157–168. Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., et al. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2(10), 861–863. Giedd, J. N., Rumsey, J. M., Castellanos, F. X., Rajapakse, J. C., Kaysen, D., Catherine Vaituzis, A., et al. (1996). A quantitative MRI study of the corpus callosum in children and adolescents. Developmental Brain Research, 91(2), 274–280. Giza, C. C., & Prins, M. L. (2006). Is being plastic fantastic? Mechanisms of altered plasticity after developmental traumatic brain injury. Developmental Neuroscience, 28(4–5), 364–379. Gläscher, J., Rudrauf, D., Colom, R., Paul, L., Tranel, D., Damasio, H., et al. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences, 107(10), 4705–4709. Haacke, E. M., Mittal, S., Wu, Z., Neelavalli, J., & Cheng, Y.-C. (2009). Susceptibilityweighted imaging: Technical aspects and clinical applications, part 1. American Journal of Neuroradiology, 30(1), 19–30. Haacke, E. M., Xu, Y., Cheng, Y. C. N., & Reichenbach, J. R. (2004). Susceptibility weighted imaging (SWI). Magnetic Resonance in Medicine, 52(3), 612–618. Hallett, T. L. (1997). Linguistic competence in paediatric closed head injury. Developmental Neurorehabilitation, 1(4), 219–228. Hart, T., Seignourel, P. J., & Sherer, M. (2009). A longitudinal study of awareness of deficit after moderate to severe traumatic brain injury. Neuropsychological Rehabilitation, 19(2), 161–176. Hattier, M. A., Matson, J. L., Belva, B. C., & Horovitz, M. (2011). The occurrence of challenging behaviours in children with autism spectrum disorders and atypical development. Developmental Neurorehabilitation, 14(4), 221–229. Hebb, D. O. (1942). The effect of early and late brain injury upon test scores, and the nature of normal adult intelligence. Proceedings of the American Philosophical Society, 275–292. Jacobs, R., Harvey, A. S., & Anderson, V. (2007). Executive function following focal frontal lobe lesions: Impact of timing of lesion on outcome. Cortex, 43(6), 792–805. Johnson, M. H., Griffin, R., Csibra, G., Halit, H., Farroni, T., De Haan, M., et al. (2005). The emergence of the social brain network: Evidence from typical and atypical development. Development and Psychopathology, 17(03), 599–619. Kennedy, D. P., & Adolphs, R. (2012). The social brain in psychiatric and neurological disorders. Trends in Cognitive Sciences, 16(11), 559–572. Kolb, B., Pellis, S., & Robinson, T. E. (2004). Plasticity and functions of the orbital frontal cortex. Brain and Cognition, 55(1), 104–115. Kraus, M. F., Susmaras, T., Caughlin, B. P., Walker, C. J., Sweeney, J. A., & Little, D. M. (2007). White matter integrity and cognition in chronic traumatic brain injury: A diffusion tensor imaging study. Brain, 130(10), 2508–2519. Levin, H. S., Benavidez, D., Verger-Maestre, K., Perachio, N., Song, J., Mendelsohn, D., et al. (2000). Reduction of corpus callosum growth after severe traumatic brain injury in children. Neurology, 54(3), 647. Li, L., & Liu, J. (2013). The effect of pediatric traumatic brain injury on behavioral outcomes: A systematic review. Developmental Medicine & Child Neurology, 55(1), 37–45. Luders, E., Thompson, P. M., & Toga, A. W. (2010). The development of the corpus callosum in the healthy human brain. The Journal of Neuroscience, 30(33), 10985–10990. McMillan, J., Beavis, A., & Jones, F. L. (2009). The AUSEI06: a new socioeconomic index for Australia. Journal of Sociology, 45(2), 123–149. Masten, A. S., & Cicchetti, D. (2010). Developmental cascades. Development and Psychopathology, 22(03), 491–495. Manly, T., Robertson, I. H., Anderson, V., & Nimmo-Smith, I. (1999). TEA-Ch: The Test of Everyday Attention for Children Manual. Bury St. Edmunds, UK: Thames Valley Test Company Ltd. Muller, F., Simion, A., Reviriego, E., Galera, C., Mazaux, J.-M., Barat, M., et al. (2010). Exploring theory of mind after severe traumatic brain injury. Cortex, 46(9), 1088–1099. Muscara, F., Catroppa, C., & Anderson, V. (2008). Social problem-solving skills as a mediator between executive function and long-term social outcome following paediatric traumatic brain injury. Journal of Neuropsychology, 2(2), 445–461. Reichenbach, J. R., Venkatesan, R., Schillinger, D. J., Kido, D. K., & Haacke, E. M. (1997). Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology, 204(1), 272–277. Rosema, S., Crowe, L., & Anderson, V. (2012). Social function in children and adolescents after traumatic brain injury: A systematic review 1989–2011. Journal of Neurotrauma, 29(7), 1277–1291. Ryan, N. P., Anderson, V., Godfrey, C., Beauchamp, M. H., Coleman, L., Eren, S., et al. (2014). Predictors of very-long-term sociocognitive function after pediatric traumatic brain injury: Evidence for the vulnerability of the immature ‘‘social brain’’. Journal of Neurotrauma, 31(7), 649–657.

N.P. Ryan et al. / Brain & Language 142 (2015) 86–95 Ryan, N. P., Anderson, V., Godfrey, C., Eren, S., Rosema, S., Taylor, K., et al. (2013). Social communication mediates the relationship between emotion perception and externalizing behaviors in young adult survivors of pediatric traumatic brain injury (TBI). International Journal of Developmental Neuroscience, 31(8), 811–819. Ryan, N. P., Catroppa, C., Cooper, J., Beare, R., Ditchfield, M., Coleman, L., et al. (2015). Relationships between acute imaging biomarkers and theory of mind impairment in post-acute pediatric traumatic brain injury: a prospective analysis using Susceptibility-Weighted Imaging (SWI). Neuropsychologia, 66, 32–38. Saxe, R. (2006). Uniquely human social cognition. Current Opinion in Neurobiology, 16(2), 235–239. Satz, P., Fletcher, J. M., Clark, W., & Morris, R. M. (1981). Lag deficit rate and delay constructs in specific learning disabilities a re-examination. In A. Ansara, N. Geschwind, A. Galaburda, M. Albert, & N. Gartrell (Eds.), Sex Differences in Dyslexia. New York: Orton Dyslexia Society. Sehgal, V., Delproposto, Z., Haacke, E. M., Tong, K. A., Wycliffe, N., Kido, D. K., et al. (2005). Clinical applications of neuroimaging with susceptibility-weighted imaging. Journal of Magnetic Resonance Imaging, 22(4), 439–450. Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., et al. (2008). Neurodevelopmental trajectories of the human cerebral cortex. The Journal of Neuroscience, 28(14), 3586–3594. Spitz, G., Maller, J. J., Ng, A., O’Sullivan, R., Ferris, N. J., & Ponsford, J. L. (2013). Detecting lesions after traumatic brain injury using susceptibility weighted imaging: A comparison with fluid-attenuated inversion recovery and correlation with clinical outcome. Journal of Neurotrauma, 30(24), 2038–2050. Stowe, L. A., Broere, C. A., Paans, A. M., Wijers, A. A., Mulder, G., Vaalburg, W., et al. (1998). Localizing components of a complex task: Sentence processing and working memory. NeuroReport, 9(13), 2995–2999. Sullivan, J. R., & Riccio, C. A. (2010). Language functioning and deficits following pediatric traumatic brain injury. Applied Neuropsychology, 17(2), 93–98. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Allyn and Bacon, MA: Needham Heights. Tasker, R. C. (2006). Changes in white matter late after severe traumatic brain injury in childhood. Developmental Neuroscience, 28(4–5), 302–308.

95

Taylor, M. J., Charman, T., Robinson, E. B., Hayiou-Thomas, M. E., Happé, F., Dale, P. S., et al. (2014). Language and traits of autism spectrum conditions: Evidence of limited phenotypic and etiological overlap. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 165(7), 587–595. Tong, K. A., Ashwal, S., Holshouser, B. A., Nickerson, J. P., Wall, C. J., Shutter, L. A., et al. (2004). Diffuse axonal injury in children: Clinical correlation with hemorrhagic lesions. Annals of Neurology, 56(1), 36–50. Van Praag, H., Kempermann, G., & Gage, F. H. (2000). Neural consequences of environmental enrichment. Nature Reviews Neuroscience, 1(3), 191–198. Watts, A. J., & Douglas, J. M. (2006). Interpreting facial expression and communication competence following severe traumatic brain injury. Aphasiology, 20(8), 707–722. Wechsler, D. (1999). WASI: Wechsler Abbreviated Scale of Intelligence. San Antonio: The Psychological Corporation. Wechsler, D. (2003). Wechsler Intelligence Scale for Children – Fourth Edition – Integrated technical and interpretative manual. San Antonio, TX: Psychological Corporation. Wigg, E. H., & Secord, W. (1989). Test of language competence – Expanded edition administrative manual: Level 1 and level 2. San Antonio, TX: The Psychological Corporation. Yeates, K. O., Bigler, E. D., Dennis, M., Gerhardt, C. A., Rubin, K. H., Stancin, T., et al. (2007). Social outcomes in childhood brain disorder: A heuristic integration of social neuroscience and developmental psychology. Psychological Bulletin, 133(3), 535. Yeates, K. O., Swift, E., Taylor, H. G., Wade, S. L., Drotar, D., Stancin, T., et al. (2004). Short- and long-term social outcomes following pediatric traumatic brain injury. Journal of the International Neuropsychological Society, 10(3), 412–426. Ylvisaker, M., Turkstra, L. S., & Coelho, C. (2005). Behavioral and social interventions for individuals with traumatic brain injury: A summary of the research with clinical implications. Paper presented at the seminars in speech and language. Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., et al. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage, 31(3), 1116–1128.