Abstract

Forty-five children born extremely preterm and/or with extremely low birth weight (ELBW), who were of average intelligence, were assessed at age 7–9 on a raft of measures of executive function (EF) designed to assess inhibition, set shifting, planning, fluency, and working memory. Relative to 45 full-term controls, the preterm/ELBW children showed reliable impairments of inhibition, fluency, and working memory. Among the 7-year olds, the preterm/ELBW group also showed significantly worse set shifting. After controlling for age and family socioeconomic status (SES), within-group analyses of the preterm/ELBW data revealed that higher birth weights were associated with better inhibition, whereas lower neurobiological risk (gauged by such aspects of neonatal medical history as a number of days on oxygen) was associated with better planning. Moreover, there were interactions between neurobiological risk and SES on the measures of inhibition, fluency, and working memory, indicating that the adverse effects of risk were greater among children from low-income households. These findings demonstrate that neonatal medical problems are associated with considerable variability in EF among normally developing preterm/ELBW children and implicate an important influence of the family environment on the maturation of EF.

Introduction

Over recent years, improvements in obstetric and neonatal care have increased the chances of survival of infants born extremely preterm (<28 weeks gestation) or with extremely low birth weight (ELBW; <1,000 g; Anderson et al., 2011). Whereas medical advances have also reduced the incidence of major physical and cognitive disabilities in these children, it is well recognized that they are at increased risk of a host of more subtle health and developmental problems that may persist into adolescence. These include behavior problems (Shum, Neulinger, O'Callaghan, & Mohay, 2008; Wood, Marlow, Costeloe, Gibson, & Wilkinson, 2000), academic underachievement (Taylor, Klein, Minich, & Hack, 2000; Johnson, Wolke, Hennessy, & Marlow, 2011), and small but reliable impairments of IQ relative to full-term children of similar socioeconomic status (SES; Anderson, Doyle, & Victorian Infant Collaborative Study Group, 2003; Briscoe, Gathercole, & Marlow, 2001).

The cognitive difficulties associated with preterm birth are likely to reflect hypoxic-ischemic events, which can both injure the brain and interfere with its subsequent development (Gardner, Karmel, Magano, Norton, & Brown, 1990; Volpe, 2008). Premature/ELBW infants are vulnerable to a variety of perinatal medical conditions, including respiratory distress syndrome (RDS) and intraventricular hemorrhage, all of which may lead to brain damage. Magnetic resonance imaging studies have revealed thinning of the corpus callosum in children and adolescents who were born preterm/ELBW (Narberhaus et al., 2007; Nosarti et al., 2004), reductions of hippocampal volume (Isaacs et al., 2000), and lesions within the prefrontal cortex (PFC) and caudate nucleus (O'Donnell & Grace, 1995).

Evidence of direct damage to the PFC coupled with disruptions to the corpus callosum in areas that interconnect the PFC has prompted suggestions that preterm/ELBW birth is likely to be a particular risk factor for the development of executive function (EF; Narberhaus et al., 2008; Vicari, Caravale, Carlesimo, Casedi, & Allemand, 2004). The term EF encompasses higher order, effortful cognitive processes that aid in the monitoring and control of purposeful thought and action, such as planning, set maintenance, organized search, flexibility of thought and action, working memory, and inhibitory control (Carlson, 2003). Additional to fronto-striatal-cerebellar circuitry, developmental and neuropsychological studies have implicated the frontal lobes of the brain, especially the dorsolateral PFC, as being intricately involved in EF (Anderson, 2002; Leh, Petrides, & Strafella, 2010). For example, evidence that the efficiency of EF continues to improve from childhood to early adulthood is usually attributed to the protracted period of maturation of the PFC (Jarman, Vavrik, & Walton, 1995; Thatcher, 1991; Visu-Petra, Benga, & Miclea, 2007).

Studies comparing preterm/ELBW children and full-term children have supported the contention that EF is impaired in the former group, even after controlling for IQ (Aarnoudse-Moens, Smidts, Oosterlaan, Duivenvoorden, & Weisglas-Kuperus, 2009; Anderson, Howard, & Doyle, 2010; Bayless & Stevenson, 2007; Bohm, Smedler, & Forssberg, 2004; Curtis, Lindeke, Georgieff, & Nelson, 2002; Marlow, Hennessy, Bracewell, & Wolke, 2007; Nosarti et al., 2007). In one such investigation, Harvey, O'Callaghan, and Mohay (1999) found robust group differences favoring full-term children on tests of planning, inhibition, and sequencing, despite equating the samples for receptive vocabulary. Likewise, Marlow and colleagues (2007) reported that preterm/ELBW children were outperformed by matched full-term controls on several subtests of the NEPSY, with differences remaining reliable after controlling for performance on the Kaufman Assessment Battery. Evidence that EF represents a distinct domain of cognitive impairment in preterm/ELBW children is noteworthy given the apparent, robust involvement of EF in many aspects of children's academic achievement. For example, research with full-term children points to sizeable contributions of inhibitory control and working memory to the development of reading and mathematics over and above the contribution of general intellectual ability (Welsh, Nix, Blair, Bierman, & Nelson, 2010).

Individual Differences in EF among Preterm/ELBW Children

Despite the dispiriting picture painted by global comparisons of EF between preterm/ELBW children and full-term children, it is apparent that there is marked heterogeneity within the former group and that many such children show no evidence of impairment (Anderson & Doyle, 2008). Although the literature examining individual differences of EF among preterm/ELBW children remains sparse, two recent meta-analyses both concluded that impairment is evident across a variety of domains but moderated by degree of prematurity. First, Mulder, Pitchford, Hagger, and Marlow (2009) reported that difficulties with inhibition, working memory, and fluency were as profound for preterm children who had a gestational age of more than 26 weeks as for preterm children who had a gestational age of 26 weeks or less, whereas difficulties with planning and set shifting were more obvious in the extremely preterm group. Second, Aarnoudse-Moens, Weisglas-Kuperus, van Goudoever, and Oosterlaan (2009) found that not only did preterm/ELBW children perform more poorly than full-term children on tests of EF and academic ability, but outcomes for attention, mathematics, and reading within the preterm/ELBW group were reliably and positively correlated with gestational age and birth weight.

Other research indicates that it is neurobiological risk rather than preterm or low-weight birth per se that that has most impact on cognitive development (see review by Luciana, 2003). For example, Downie, Frisk, and Jakobson (2005) reported that evidence of impaired performance on academic and cognitive tests among preterm/ELBW children was limited to participants with periventricular brain damage and that participants without such damage performed at levels indistinguishable from those of full-term controls. Similarly, Curtis and colleagues (2002) observed that EF and general cognitive ability in preterm/ELBW children (mean age = 11 years) were predicted by risk as estimated by each participant's neonatal history of medical complications. In their investigation, higher levels of risk were associated with lower IQ, shorter digit span, and poorer performance on Block Design, Tower of London, and tests of spatial recognition and spatial working memory. These findings accord with the conclusion that medical problems experienced by preterm/ELBW infants can exacerbate the chances of disrupted cortical development and brain connectivity (Aarnoudse-Moens, Weisglas-Kuperus, et al., 2009; Weindrich, Jennen-Steinmetz, Laucht, & Schmidt, 2003).

Other contributors to individual differences in EF among preterm/ELBW children include age at the time of assessment and family SES. Research into the effects of age has produced a varied set of findings. Although it is usually reported that preterm/ELBW children show developmental “catch up” in EF, some studies have observed a stable impairment relative to full-term children, or even more pronounced impairment at older ages (review by Mulder et al., 2009). It has also been reported that developmental trends in EF are moderated by gestation such that children who are extremely preterm are less likely to show amelioration of their EF problems as they grow older, especially problems relating to inhibition and spatial working memory.

In terms of SES, preterm/ELBW children mimic full-term children in displaying poorer cognitive and academic development if they are raised in low- as opposed to high-income households (Landry, Denson, & Swank, 1997; Roberts, Bellinger, & McCormick, 2007). Indeed, it has been reported that SES is the strongest predictor of school readiness in preterm/ELBW children, eclipsing the influence of biomedical risk factors (Patrianakos-Hoobler, Msall, Marks, Huo, & Schreiber, 2009). Researchers have also noted a synergistic effect of biological and environmental stress, with the adverse influences of perinatal complications being exacerbated for children who grow up in poverty (Aylward, 1992; Taylor, Klein, Schatschneider, & Hack, 1998). Although few investigations have focused specifically on EF, Robson and Pederson (1997) noted that the quality of preterm/ELBW children's home environment was a reliable predictor of observational ratings of inattention, impulsivity, and hyperactivity. Similarly, Kelly, Nazroo, McMunn, Boreham, and Marmot (2001) showed that a disadvantaged social environment increased the chances of hyperactivity and conduct disorder among preterm children. In a recent study that used the level of maternal education as the indicator of SES, Aarnoudse-Moens, Smidts, and colleagues (2009) found that SES was linked positively with results of a laboratory measure of preterm children's inhibitory control. These findings accord with research indicating that the development of EF may be malleable, most likely reflecting the protracted period of post-natal maturation shown by the prefrontal regions of the brain and connections with the limbic system that make such regions susceptible to stressful life events (review by Blair, 2006). Additionally, it has been suggested that high SES homes are more likely to provide children with responsive one-on-one social interactions with their primary caregivers that help them to (1) practise skills in sustained attention and (2) develop internalized language that supports planning and other forms of strategic behavior (Bernier, Carlson, & Whipple, 2010; Landry, Miller-Loncar, Smith, & Swank, 2002).

The Present Study

The present study formed part of a continuing, large-scale investigation of cognitive development in extremely preterm/ELBW children. We describe here the findings of several tests of EF administered to a group of such children aged 7–9, with their performance being compared with that of a full-term, age-matched control group. Because previous research has typically focused on a narrow range of skills within any single sample, we assessed our participants on a comprehensive battery of tests designed to gauge all major components of EF, namely, inhibition, set shifting, planning, fluency, and working memory. Members of our preterm/ELBW group were all attending mainstream school, most without repeating any grades, and had been assessed at age 4 as having average cognitive ability. The decision to exclude children with intellectual impairment was based on the fact that the majority of survivors of preterm/ELBW birth are free of severe learning disabilities. We therefore wanted to focus on the impact of preterm/ELBW birth on the EF of children who were making satisfactory academic progress at school and who had no major neurological or sensory problems (e.g., Bayless & Stevenson, 2007). We predicted that group differences in EF performance (if any) would favor the full-term children, especially in the case of the younger participants.

Our second aim was to examine individual differences in EF within the preterm/ELBW group as a function of biomedical variables (birth weight, and estimates of neurobiological risk derived from the child's medical history) and SES (gauged by mother's highest level of education). We used birth weight as our measure of prematurity given a broader range of scores on this variable (571–1,192 g) than gestational age (24–31 weeks). In any case, birth weight and gestational age were reliably correlated (r = .35, p < .05). In terms of the biomedical factors, we expected to find poorer outcomes associated with lower birth weight and higher neurobiological risk scores. In terms of SES, we assumed that EF would be superior for children whose mothers reported higher levels of education.

Importantly, we also explored the two-way interactions between the biomedical variables and SES (i.e., birth weight × SES, neurobiological risk × SES). Because no previous investigations have examined whether SES moderates the impact of biomedical risk on the development of EF, we aimed in the present study to provide some preliminary evidence on this point. Any interactions pointing to a diminished impact of biomedical risk in children growing up in higher SES households may constitute important evidence of plasticity in the maturation of EF that could be used to inform the design of early cognitive interventions for preterm/ELBW children.

Method

Participants

Extremely preterm/ELBW group

Potential participants were identified from the database of the Growth and Development Unit at the children's hospital according to the following criteria: (1) aged between 7 and 9 at the time of assessment; (2) currently attending mainstream school; (3) GQ > 85 on the McCarthy Scales of Children's Abilities when assessed at 4 years of age; and (4) no significant known physical or neurological disabilities. Of 60 children in the database who fulfilled these criteria, the parents of 45 consented for their child to participate in the study (22 males and 23 females; 19, 7-year olds; 16, 8-year olds; and 10, 9-year olds). These 45 children ranged in gestation from 24 to 31 weeks and in birth weight from 571 to 1,192 g. There were 26 children classified as both extremely preterm (<28 weeks) and ELBW (<1,000 g), 6 children classified as extremely preterm (<28 weeks) and very low birth weight (VLBW; between 1,000 and 1,192 g), and 13 children classified as very preterm (between 28 and 31 weeks gestation) and ELBW (<1,000 g).

Two children were on medication to control ADHD symptoms and their parents were requested to refrain from administering this medication on the day of testing. Participants were Caucasian apart from one child of Indian origin. None of the children had hearing loss and those with vision problems wore corrective spectacles. Details pertaining to each child's neonatal medical history were used to estimate their level of neurobiological risk based on specific medical problems and general severity of illness. Specific medical problems included whether the child had RDS (1 = yes, N = 31; 0 = no, N = 14) and patent ductus arteriosus (PDA: 1 = yes, N = 25; 0 = no, N = 20). Severity of illness was estimated in terms of days on oxygen (continuous variable ranging from 0 to 128 days; M = 58.84; SD = 36.41), days on intermittent positive-pressure ventilation (IPPV: continuous variable ranging from 0 to 63 days; M = 16.07; SD = 15.40), and whether the child was discharged home on oxygen (1 = yes, N = 6; 0 = no, N = 39). Data on each individual risk variable were converted to z-scores, with results then being averaged across the variables to create a mean score for neurobiological risk. Only one child in our sample had necrotizing enterocolitis (Level 1), only one child had periventricular leukomalacia (PVL: non-cystic), only five children had ventricular dilatation (all mild), and only eight children had cerebral intraventricular hemorrhage (CVH: seven rated as mild and one as moderate); given the limited variability on these measures they were not included in our global estimate of risk.

Existing databases from the Growth and Development Unit of the hospital provided information about the mothers' marital status, highest level of education, age at time of child's birth, and hospital status (public versus private patient). Of the 45 mothers, 40 were married (89%) and 5 were in a de facto relationship (11%). There were 10 mothers who did not complete secondary school (score = 1; 22%), 11 mothers who completed secondary school (score = 2; 24%), 9 mothers who completed some further education (score = 3; 20%), and 15 mothers who completed university (score = 4; 33%). The mean age of the mothers at the time of their child's birth was 29.7 years (SD = 4.5 years). There were 28 mothers who were public patients and 17 mothers who had private health insurance. Reinforcing our intention to use maternal education as a gauge of SES, mothers with health insurance had a significantly higher level of education than the mothers with no health insurance (M = 3.12, SD = 0.93 vs. M = 2.36, SD = 1.22), t(43) = 2.20, p = .033.

Full-term control group

The full-term control group comprised 45 children (23 males and 22 females; 20, 7-year olds; 14, 8-year olds; and 11, 9-year olds) who were born with 37 or more weeks of gestation (range = 37–42 weeks) and with birth weights of at least 2,500 g (range = 2,608–4,536 g). All were Caucasian, aged 7–9 at the time of testing, free from known significant physical or neurological disabilities, and attending mainstream schools. The majority of the control participants were recruited from the friends and siblings of the preterm/ELBW children, with the aim of matching our groups as closely as possible for SES. This sample was supplemented by a small number of participants who were recruited from the general community in response to requests to take part in the study (and who represented a wide range of socioeconomic backgrounds). None suffered hearing loss and those with vision problems wore corrective spectacles. Table 1 presents group means and standard deviations for chronological age, gestational age, birth weight, and school grade. As expected, the preterm/ELBW children had significantly lower gestational ages and birth weights than did the full-term children.

Table 1.

Demographic characteristics of children in the preterm/extremely low birth weight and full-term control groups

Dependent variables Preterm/ELBW
 
Full term
 
t (88) p-value forumla 
 M SD M SD    
Age at time of test (months) 99.91 11.37 98.88 10.96 0.44 .662 0.00 
Birth weight (g) 838.24 151.70 3,591.69 486.43 −36.25 <.001 0.94 
Gestation period (weeks) 26.44 1.88 39.84 1.41 −38.25 <.001 0.94 
Current school grade 2.71 1.06 2.87 0.92 −0.74 .459 0.01 
Dependent variables Preterm/ELBW
 
Full term
 
t (88) p-value forumla 
 M SD M SD    
Age at time of test (months) 99.91 11.37 98.88 10.96 0.44 .662 0.00 
Birth weight (g) 838.24 151.70 3,591.69 486.43 −36.25 <.001 0.94 
Gestation period (weeks) 26.44 1.88 39.84 1.41 −38.25 <.001 0.94 
Current school grade 2.71 1.06 2.87 0.92 −0.74 .459 0.01 

Materials and Procedure

This study was approved by the human research ethics committee of the university and hospital and was conducted in compliance with the guidelines provided by the National Health and Medical Research Council of Australia. Written informed consents were provided by parents of the children prior to testing. Children were tested individually in a quiet location at the Psychology Clinic of the university. Tests were administered in the same order for all participants and took 1.5–2 h to complete, with rest breaks when needed. All children completed all tests with the exception of the Trail Making Test (TMT), which could not be administered to five participants in the preterm/ELBW group and two participants in the full-term group due to time constraints. The EF tasks were part of a larger battery of tests and questionnaires administered to the children and their parents that additionally assessed children's attention and behavior problems.

Cognitive inhibition

Cognitive inhibition was assessed using Golden's (1978) version of the Stroop Test. Results were scored by dividing the number of colors named in the CW-Block by the number of colors named in the C-Block and then multiplying by the number of words read in the W-block, with higher scores indicating better inhibition.

Set shifting

Cognitive set shifting (or flexibility) was assessed using the TMT (Reitan & Wolfson, 1993; Spreen & Strauss, 1998). The measure obtained for this test was the difference between the time taken to complete part B and A (i.e., Trail B − Trail A), with lower scores indicating better performance.

Planning

Children's planning ability was assessed using the four-disc version of the Tower of London (Shum et al., 2009). They were presented with a target picture showing the discs in a particular arrangement on the poles and requested to rearrange the discs to match the target picture within a set number of moves. The task was designed to become more difficult over successive trials and was discontinued if the child failed two consecutive trials. Participants were given three attempts at each trial. They received 3 points if they solved the problem on the first attempt, 2 points if they needed two attempts, 1 point if they succeeded by the third attempt, and 0 point if they failed the task. The overall score for this test was obtained by summing the points for each trial.

Fluency

Fluency was assessed using the Controlled Oral Word Association Test (COWAT; Spreen & Strauss, 1998). In the COWAT FAS version, used to assess letter fluency, the child is requested to generate verbally as many words as possible that begin with a specific letter (F, A, and S) within a time limit of 1min per letter. Proper nouns and words with a different suffix are deemed inadmissible. Children's score for COWAT FAS was the total number of acceptable words generated over three minutes.

Working memory

Working memory was assessed using verbal and spatial tasks. The verbal task comprised the Digits Backward subtest from the Wechsler Intelligence Scale for Children-Third Edition (WISC-III; Wechsler, 1991). The spatial task comprised the Spatial Span Backward subtest from the WISC-III as a Process Instrument (Kaplan, Fein, Kramer, Delis, & Morris, 1999). Raw scores from each test were converted to z-scores and averaged to create an overall measure of working memory.

Results

Results were subjected to (1) correlation analyses that examined associations among the EF measures, (2) multivariate analysis of variance that compared EF performance between the preterm/ELBW group and the full-term group as a function of age, and (3) correlation and regression analyses that evaluated the main and interactive effects of the biomedical variables and SES on EF scores within the preterm/ELBW group. Prior to the regression analyses, data on the EF measures were converted to age-corrected z-scores. All measures were normally distributed and there were no outliers.

Relations Between EF Measures

Zero-order correlations conducted for the whole sample revealed that EF performance were robustly and positively related to age, all p values <.01. In further analyses, we examined the partial correlations between the EF measures after controlling for age, separately for each group. Results showed that the measures were largely dissociated apart from significant correlations between COWAT FAS and working memory in the preterm/ELBW group, r(45) = .36, p = .017, and COWAT FAS and Stroop in the full-term group, r(45) = .46, p = .001.

Between-Group Comparisons of EF

Table 2 shows means and standard deviations of performance on the individual measures of EF as a function of group and age. Data for the Stroop Test, COWAT FAS, Tower of London, and working memory were analyzed using a 2 (group: preterm/ELBW vs. full-term) × 3 (age in years: 7 vs. 8 vs. 9) MANOVA. Data for the TMT were analyzed separately given the smaller sample size. The MANOVA revealed a significant multivariate main effect for age, Wilks' λ = 0.611, F(8, 162) = 5.65, p < .001, forumla = 0.22, and a significant multivariate main effect for group, Wilks' λ = 0.871, F(4, 81) = 2.99, p = .024, forumla = 0.13, but no interaction, Wilks' λ = 0.888, F(8, 162) = 1.24, p = . 279, forumla = 0.06. Given the significance of the overall test, the univariate main effects were examined. Significant univariate main effects of age emerged for all measures (p-values <.01). Significant univariate group differences favoring the full-term children emerged for the Stroop Test (28.11 vs. 32.42), F(1, 84) = 4.70, p = .033, forumla = 0.05, COWAT FAS (15.93 vs. 19.56), F(1, 84) = 5.90, p = .017, forumla = 0.07, and working memory (−0.27 vs. 0.15), F(1, 84) = 7.63, p = .007, forumla = 0.08. A 2 (group: preterm/ELBW vs. full-term) × 3 (age in years: 7 vs. 8 vs. 9) ANOVA applied to results of the TMT revealed a significant interaction between group and age, F(2, 77) = 3.38, p = .039, forumla = 0.08. Post-hoc comparisons uncovered a significant group difference favoring the full-term children only in the case of the 7-year olds, p < .05.

Table 2.

Group means and standard deviations of performance on tests of executive function

Tests of executive function Preterm/ELBW
 
Full term
 
 M SD M SD 
Stroop 
 7-year olds 21.93 6.85 28.70 6.92 
 8-year olds 31.31 9.16 33.73 7.92 
 9-year olds 34.75 9.87 37.49 11.18 
TMT (B-A) 
 7-year olds 81.90 66.79 47.01 36.99 
 8-year olds 42.09 26.38 41.80 37.68 
 9-year olds 28.67 19.68 29.18 18.82 
TOL 
 7-year olds 14.53 4.79 14.40 3.94 
 8-year olds 14.44 3.08 16.64 3.43 
 9-year olds 17.90 2.85 18.09 2.43 
COWAT FAS 
 7-year olds 13.68 5.87 17.75 7.68 
 8-year olds 15.75 4.16 19.79 7.17 
 9-year olds 20.50 5.48 22.55 7.02 
Working Memory 
 7-year olds −0.83 0.70 −0.11 0.63 
 8-year olds 0.17 0.84 0.15 0.67 
 9-year olds 0.08 0.65 0.62 0.59 
Tests of executive function Preterm/ELBW
 
Full term
 
 M SD M SD 
Stroop 
 7-year olds 21.93 6.85 28.70 6.92 
 8-year olds 31.31 9.16 33.73 7.92 
 9-year olds 34.75 9.87 37.49 11.18 
TMT (B-A) 
 7-year olds 81.90 66.79 47.01 36.99 
 8-year olds 42.09 26.38 41.80 37.68 
 9-year olds 28.67 19.68 29.18 18.82 
TOL 
 7-year olds 14.53 4.79 14.40 3.94 
 8-year olds 14.44 3.08 16.64 3.43 
 9-year olds 17.90 2.85 18.09 2.43 
COWAT FAS 
 7-year olds 13.68 5.87 17.75 7.68 
 8-year olds 15.75 4.16 19.79 7.17 
 9-year olds 20.50 5.48 22.55 7.02 
Working Memory 
 7-year olds −0.83 0.70 −0.11 0.63 
 8-year olds 0.17 0.84 0.15 0.67 
 9-year olds 0.08 0.65 0.62 0.59 

Notes: TMT (B-A) = Trail Making Test difference scores; high scores represent inferior performance; TOL = Tower of London; COWAT = Controlled Oral Word Association Test.

Main and Interactive Effects of Biomedical Factors and SES within the Preterm/ELBW Group

Table 3 shows the full correlations between the dependent variables (individual measures of EF) and the predictor variables (birth weight, biomedical risk, and SES), as well as partial correlations between all pairs of variables after controlling for age. Whereas birth weight was linked negatively with neurobiological risk, there were no other significant correlations between the predictor variables. In terms of the relationships between the predictor variables and EF, neurobiological risk showed a negative association with results for Tower of London, whereas birth weight showed a positive association with results for the Stroop Test. For none of the predictor variables were outcomes significantly related to age; age × SES r(45) = −.09 p = .556, age × birth weight r(45) = −.22 p = .147, age × risk r(45) = −.24 p = .105.

Table 3.

Correlations between the predictor and dependent variables in the preterm/extremely low birth weight group

 
1. SES —       
2. Birth weight −.17 (−.20—      
3. Neurobiological risk .08 (.06−.47** (−.56**) —     
4. Stroop .02 (.08.14 (.31*) −.15 (.00—    
5. TMT (B-A) −.01 (−.05.09 (−.04.18 (.05−.23 (.08—   
6. TOL −.09 (−.07.11 (.18−.44** (−.39**) .35* (.26−.20 (−.05—  
7. COWAT FAS .08 (.14−.11 (−.01−.07 (.06.37* (.17−.22 (.02.22 (.12— 
8. Working memory .15 (.23−.17 (−.07−.06 (.10.48** (.28−.34* (−.10.23 (.11.51** (.36*) 
 
1. SES —       
2. Birth weight −.17 (−.20—      
3. Neurobiological risk .08 (.06−.47** (−.56**) —     
4. Stroop .02 (.08.14 (.31*) −.15 (.00—    
5. TMT (B-A) −.01 (−.05.09 (−.04.18 (.05−.23 (.08—   
6. TOL −.09 (−.07.11 (.18−.44** (−.39**) .35* (.26−.20 (−.05—  
7. COWAT FAS .08 (.14−.11 (−.01−.07 (.06.37* (.17−.22 (.02.22 (.12— 
8. Working memory .15 (.23−.17 (−.07−.06 (.10.48** (.28−.34* (−.10.23 (.11.51** (.36*) 

Notes: Partial correlations, controlling for age, are shown in bold. TMT (B-A) = Trail Making Test difference scores; SES = socioeconomic status; TOL = Tower of London; COWAT = Controlled Oral Word Association Test.

*p < .05, **p < .01

Additionally, we examined the associations between the individual components of neurobiological risk and EF. In the case of the continuous risk variables, we calculated partial correlations between the measures of risk and the measures of EF, controlling for age and SES. For the categorical risk variables, we performed ANCOVAs, again controlling for age and SES. Significant results emerged only in the case of Tower of London. Higher scores on Tower of London were associated with fewer days on oxygen (partial r = −.32, p = .041) and with IPPV (partial r = −.43, p = .010), in the latter case even after controlling for weeks of gestation as well (partial r = −.38, p = .014). There was a marginal difference in Tower of London performance between children who had PDA and those who did not (Adj. M = 14.22 vs. Adj. M = 16.52), F(1, 41) = 3.64, p = .063, forumla = 0.08, an effect that disappeared after further controlling for time on IPPV (Adj. M = 15.03 vs. Adj. M = 15.57), F(1, 38) = 0.13, p = .718, forumla = 0.00.

To evaluate the possible interactive effects of the biomedical factors and SES, we conducted a series of nested hierarchical regression analyses—one for each of the individual measures of EF. In all cases, the predictors entered on Step 1 were birth weight, neurobiological risk, and SES; and the predictors entered on Step 2 were the interactions between SES and birth weight and between SES and neurobiological risk. For the purpose of these analyses, we reverse scored the neurobiological risk data such that higher scores indicated lower risk.

Results of the regression analyses are summarized in Table 4. There was a significant main effect of birth weight on Stroop performance, confirming our earlier observation that children with higher birth weights showed better inhibitory skills. There was a marginal main effect of neurobiological risk on Tower of London, confirming our earlier observation that children with higher estimates of risk showed poorer planning. There were three significant interactions in total, all involving SES and neurobiological risk. Specifically, the interactions were evident for the Stroop Test, COWAT FAS, and working memory. In all cases, the nature of the interaction was such that the adverse impact of neurobiological risk on EF performance was ameliorated among children from higher as opposed to lower SES families.

Table 4.

Summary of hierarchical regression analyses for variables predicting executive function in the preterm/extremely low birth weight group

Dependent variable Adj. R2 R2 Overall F F change Unique predictors 
Stroop 
 Model 1 .02 .08 1.21 1.21 Weight (β = 0.31, p = .077) 
 Model 2 .09 .11 1.90  2.78+ Weight (β = 0.36, p = .038), SES × Risk (β = 0.41, p = .027) 
TMT (B-A) 
 Model 1 −.05 .03 0.19 0.33 — 
 Model 2 −.09 .03 0.20 0.39 — 
TOL 
 Model 1 .04 .11 1.65 1.65 Risk (β = 0.31, p = .070) 
 Model 2 .00 .00 0.95 0.01 Risk (β = 0.31, p = .083) 
COWAT FAS 
 Model 1 −.05 .02 0.26 0.26 — 
 Model 2 .09 .17 1.81 4.08* SES × Risk (β = 0.36, p = .049) 
Working Memory 
 Model 1 .00 .07 0.98 1.03 — 
 Model 2 .09 .19 1.82 2.95+ SES × Risk (β = 0.42, p = .022) 
Dependent variable Adj. R2 R2 Overall F F change Unique predictors 
Stroop 
 Model 1 .02 .08 1.21 1.21 Weight (β = 0.31, p = .077) 
 Model 2 .09 .11 1.90  2.78+ Weight (β = 0.36, p = .038), SES × Risk (β = 0.41, p = .027) 
TMT (B-A) 
 Model 1 −.05 .03 0.19 0.33 — 
 Model 2 −.09 .03 0.20 0.39 — 
TOL 
 Model 1 .04 .11 1.65 1.65 Risk (β = 0.31, p = .070) 
 Model 2 .00 .00 0.95 0.01 Risk (β = 0.31, p = .083) 
COWAT FAS 
 Model 1 −.05 .02 0.26 0.26 — 
 Model 2 .09 .17 1.81 4.08* SES × Risk (β = 0.36, p = .049) 
Working Memory 
 Model 1 .00 .07 0.98 1.03 — 
 Model 2 .09 .19 1.82 2.95+ SES × Risk (β = 0.42, p = .022) 

Notes: Step 1 predictors = SES, birth weight, neurobiological risk; Step 2 predictors = SES × Weight, SES × Risk. Dependent variable are age-corrected z-scores. TMT (B-A) = Trail Making Test difference scores; TOL = Tower of London; COWAT = Controlled Oral Word Association Test; SES = socioeconomic status.

Neurobiological Risk is reverse scored such that higher scores indicate lower risk.

+p < .10.

*p < .05.

In those two instances where the biomedical variables showed a main effect on EF performance, we conducted one-way ANCOVAs, controlling for age, to gain an idea of where such performance diverged from that of the full-term group. For Tower of London, the independent variable was neurobiological risk with four levels; full term (N = 45), lower risk preterm/ELBW (N = 15), moderate risk preterm/ELBW (N = 15), and higher risk preterm/ELBW (N = 15). The risk subcategories for the preterm/ELBW children were determined by rank ordering the z-scores and dividing them into three groups of equal size. For Stroop, the independent variable was birth weight with four levels; full term (N = 45), weights of 892–1,192 g (N = 15), weights of 768–874 g (N = 15), and weights of 497–763 g (N = 15). The birth weight subcategories for the preterm/ELBW children were determined by rank ordering the weight scores and dividing them into three groups of equal size. For all analyses, planned contrasts compared the performance of the full-term group with each subgroup of preterm/ELBW children (see Table 5 for descriptive statistics). Results showed that the full-term children outperformed the higher risk preterm/ELBW children on Tower of London (contrast estimate = −2.87, p = .012). Similarly, the full-term children outperformed the ≤763 g subgroup on the Stroop Test (contrast estimate = −5.80, p = .043) and scored marginally higher than the 768–874 g subgroup (contrast estimate = −5.00, p = .081). However, the performance of the full-term children failed to differ reliably from that of the lower/moderate risk preterm/ELBW children (lower risk contrast estimate = 0.93, p = .408; moderate risk contrast estimate = −0.33, p = .767), or from the preterm/ELBW children who weighed >874 g (contrast estimate = −2.10, p = .460).

Table 5.

Descriptive statistics for executive function in the preterm/ELBW group as a function of risk and birth weight

 Mean SD Adj. meana SE 
TOL performance as a function of neurobiological risk 
 Highest risk (N = 15) 13.13 4.07 13.26 0.93 
 Moderate risk (N = 15) 15.67 4.29 15.74 0.93 
 Lowest risk (N = 15) 16.93 2.89 16.58 0.94 
 Full term (N = 45) 16.00 3.73 16.05 0.54 
Stroop performance as a function of birth weight 
 Lowest weight (N = 15) 26.61 9.32 25.26 2.19 
 Moderate weight (N = 15) 27.41 11.19 27.35 2.17 
 Highest weight (N = 15) 30.32 9.37 31.11 2.18 
 Full term (N = 45) 32.42 8.49 32.62 1.26 
 Mean SD Adj. meana SE 
TOL performance as a function of neurobiological risk 
 Highest risk (N = 15) 13.13 4.07 13.26 0.93 
 Moderate risk (N = 15) 15.67 4.29 15.74 0.93 
 Lowest risk (N = 15) 16.93 2.89 16.58 0.94 
 Full term (N = 45) 16.00 3.73 16.05 0.54 
Stroop performance as a function of birth weight 
 Lowest weight (N = 15) 26.61 9.32 25.26 2.19 
 Moderate weight (N = 15) 27.41 11.19 27.35 2.17 
 Highest weight (N = 15) 30.32 9.37 31.11 2.18 
 Full term (N = 45) 32.42 8.49 32.62 1.26 

Notes: Subcategories of neurobiological risk and birth weight for the preterm/ELBW children were determined by rank ordering the z-scores from highest to lowest and dividing the sample into three groups of equal size. TOL = Tower of London; ELBW = extremely low birth weight.

aAdjusted for chronological age

Summary of Findings

Table 6 summarizes the main findings. As shown, the full-term group outperformed the preterm/ELBW group on three of five measures of EF (Stroop, COWAT FAS, and working memory), plus the two remaining measures when considering just the 7-year olds (TMT) or when compared solely with the higher risk preterm/ELBW children (Tower of London). Within-group analyses of the preterm/ELBW data uncovered reliable associations between EF performance and the biomedical variables, particularly neurobiological risk. Finally, evidence emerged that the adverse effects of neurobiological risk were attenuated for preterm/ELBW children growing up in higher as opposed to lower SES households.

Table 6.

Summary of findings

Measures of EF Between-group comparisons Within-group predictors of EF in preterm/ELBW children 
Stroop FT > PT, FT ≈ high-weight PT Birth weight, SES × Neurobiological risk 
TMT (B-A) FT 7-year olds > PT 7-year olds  
TOL FT > high-risk PT Neurobiological risk 
COWAT FAS FT > PT SES × Neurobiological risk 
Working memory FT > PT SES × Neurobiological risk 
Measures of EF Between-group comparisons Within-group predictors of EF in preterm/ELBW children 
Stroop FT > PT, FT ≈ high-weight PT Birth weight, SES × Neurobiological risk 
TMT (B-A) FT 7-year olds > PT 7-year olds  
TOL FT > high-risk PT Neurobiological risk 
COWAT FAS FT > PT SES × Neurobiological risk 
Working memory FT > PT SES × Neurobiological risk 

Notes: FT = full term; ELBW = extremely low birth weight; PT = preterm/ELBW; TMT (B-A) = Trail Making Test difference scores; TOL = Tower of London; COWAT = Controlled Oral Word Association Test; SES = socioeconomic status; EF = executive function.

Discussion

Consistent with previous research, the most obvious differences in EF between our extremely preterm/ELBW group and the full-term controls emerged for tests of inhibition, fluency, and working memory (see review by Mulder et al., 2009). Given that our preterm/ELBW sample was selected for average intelligence and freedom from obvious neurological problems, the present findings are important in highlighting EF as a distinct area of vulnerability in the cognitive development of such children. They accord with evidence from numerous studies that EF impairments in children born preterm/ELBW are robust even after controlling for general intellectual ability (e.g., Aarnoudse-Moens, Smidts, et al., 2009; Bayless & Stevenson, 2007).

Nevertheless, our global comparison of the two groups masked important differences in EF performance among the preterm/ELBW children due to birth weight, neurobiological risk, and SES. Within-group analyses of the preterm/ELBW data revealed a positive impact of birth weight on Stroop performance and a negative impact of neurobiological risk on Tower of London performance, such that children with higher birth weights (top one third of weights) matched the full-term children on inhibitory control, whereas children with lower neurobiological risk (bottom two thirds of risk scores) matched the full-term children on planning skills. Moreover, despite no significant main effect of SES on any of the measures of EF, the adverse effects of neurobiological risk were moderated by SES in the cases of the Stroop Test, COWAT FAS, and Digits/Spatial Span Backwards. These reliable interactions between SES and risk showed that impairments of inhibition, fluency, and working memory were less apparent for those preterm/ELBW children growing up in higher rather than lower SES families. The findings thus bolster a growing body of research exposing marked variability in the cognitive sequelae of preterm/ELBW birth depending on the degree of prematurity, birth weight, and history of post-natal medical complications (Curtis et al., 2002; Downie et al., 2005; meta-analysis by Aarnoudse-Moens, Weisglas-Kuperus, et al., 2009) as well as socio-demographic factors (Aarnoudse-Moens, Smidts, et al., 2009; Roberts et al., 2007; Robson & Pederson, 1997; Taylor et al., 1998).

In the present instance, the fact that such relations were detected in a sample of preterm/ELBW children who were coping satisfactorily in mainstream school raises questions about the real-world impact of EF problems that occur in the context of average intelligence. Based on previous research, it seems reasonable to assume that such problems will nonetheless underlie significant individual differences in academic performance. Numerous studies with full-term children have shown that not only are EF skills a robust predictor of school readiness (e.g., Ford, McDougall, & Evans, 2009; Rimm-Kaufman, Pianta, & Cox, 2000), they are strongly correlated with children's attainments in core domains like reading and mathematics during elementary school—even after controlling for general intelligence (e.g., Clark, Pritchard, & Woodward, 2010; St Clair-Thompson & Gathercole, 2006). In one investigation that focused on the academic results of preterm/ELBW children, Downie and colleagues (2005) found that working memory was a significant predictor of variance in reading and spelling scores, with these relations remaining robust after holding constant the effects of full-scale IQ. Because all their participants were of average intellectual ability and from predominantly middle-class backgrounds, these findings illustrate the importance of EF to school learning in survivors of preterm/ELBW birth who are free from major cognitive impairment. More recently, in a study that examined academic progress among a diverse group of preterm/ELBW children, it was further demonstrated that indices of neonatal illness contributed to the prediction of reading and mathematics attainment independently of EF (Johnson et al., 2011).

The fact that our research focused on relatively narrow age range (i.e., 7–9-year olds only) no doubt accounts for the fact that we uncovered minimal evidence of group by age interactions in EF, with such an effect emerging only for our measure of set shifting. Specifically, findings from the TMT indicated that the superior performance of the full-term group was limited to the 7-year olds. Previous studies have found that impairments of EF in preterm/ELBW children are more obvious for younger than older age groups, although the opposite trend has also been reported (review by Mulder et al., 2009). Although longitudinal studies are needed to draw definitive conclusions regarding the recovery or “catch up” of cognitive functions in preterm/ELBW children as they grow older, the present cross-sectional data for the TMT are consistent with suggestions that the human brain exhibits a degree of plasticity of function and organization and, to some extent, is capable of overcoming early injury by salvaging damaged circuits and reprogramming brain–behavior relations (e.g., Luciana, 2003). In previous research, for example, it was shown that adolescents who were born very preterm exhibited normal EF regardless of whether they had obvious markers of brain damage (Abernethy, Palaniappan, & Cooke, 2002; Rushe et al., 2001).

Notably, the only previous reports of synergistic effects of biological and environmental stressors on the cognitive development of preterm/ELBW children have emerged for measures of general intellectual ability (Aylward, 1992; Taylor et al., 1998). In yielding novel evidence of significant interactions between SES and neurobiological risk in relation to outcomes for Tower of London, COWAT FAS, and working memory, the present findings suggest that environmental factors linked with family SES have the further potential to moderate the impact of preterm/ELBW birth on the development of EF in particular. Whereas it could be argued that maternal education really tapped individual differences in IQ rather than SES and thus reflected an influence of general ability on children's performance on the EF tests, we believe that this cannot fully account for the present findings. First, the fact that mothers with higher levels of schooling were more likely to have private health insurance indicates that maternal education was a valid indicator of SES. Second, it is widely agreed that EF relies on brain systems and processes that are distinct from those underpinning general intelligence and, thus, that the development of EF is independent of IQ (see review by Blair, 2006).

Ample research with full-term children indicates that those from more advantaged backgrounds outperform those from less advantaged backgrounds on tests of EF, despite no obvious SES differences in many other domains of cognition (Noble, Norman, & Farah, 2005). Such findings have been attributed to three major environmental influences on the development of EF; general cognitive stimulation (exposure to books and toys), psychological stress (household chaos and conflict), and parenting styles (responsive versus neglectful; Bernier et al., 2010; Farah et al., 2008; Schroeder & Kelley, 2009). In relation to parenting styles, evidence that the maturation of EF during early childhood is positively correlated with maternal scaffolding (e.g., Landry et al., 2002) is consistent with the notion that children learn self-regulation and many other aspects of EF by internalizing skills that are conveyed through social discourse (i.e., routines, symbolic systems, and other cultural tools), especially skills that are verbally coded (Vygotsky, 1978).

In one study that compared the cognitive development of full-term children and preterm/VLBW children, Landry, Smith, Swank, Assel, and Vellett (2001) found that responsive parenting in the former group was associated with reduced evidence of cognitive impairment. Specifically, maternal behaviors were observed at ages 6, 12, and 24 months, and 3 and 4 years, to provide measures of acceptance, flexibility/responsiveness, and verbal and non-verbal stimulation. At the same time, children were assessed for general cognitive and language skills. Results showed that maternal responsiveness was linked positively with cognitive and language development but that this relation was moderated by birth status, such that an overall group difference favoring the full-term children was attenuated in the case of those children whose mothers provided more rather than less responsive care giving. In a brain imaging study of some of these children conducted when they reached adolescence, Frye, Malmberg, Swank, Smith, and Landry (2010) reported that preterm/VLBW birth was characterized by regional and hemispheric differences in cortical thickness and surface area. There was also a main effect of early parenting style, such that participants with whose mothers were inconsistently responsive during their early childhood showed greater overall cortical thickness and greater asymmetry in cortical thickness relative to participants whose mothers were either consistently responsive or unresponsive. Despite no reliable interaction between maternal responsiveness and birth status in the follow-up study, these correlational findings merit attention in suggesting that mothers' characteristic ways of interacting with their young children have long-term consequences for the children's brain development (see also, Forcada-Guex, Pierrehumbert, Borghini, Moessinger, & Muller-Nix, 2006).

Knowledge of the kinds of childhood experiences that shape brain function in preterm/ELBW children is essential for designing treatment strategies that will remediate impairments of EF. Evidence is starting to emerge that preterm infants whose parents receive sensitivity training while they are still in neonatal intensive care show enhanced maturation and connectivity of white matter at discharge relative to infants whose parents did not receive such training (e.g., Milgrom et al., 2010). Whereas the long-term consequences for cognitive development are unknown, research with children who are full term has shown robust benefits to EF from various kinds of training programs administered during the preschool years, including computer-based tasks (Dowsett & Livesey, 2000; Rueda, Rothbart, McCandliss, Saccomanno, & Posner, 2005), home-based interventions that encourage scaffolded educational interactions between children and their caregivers (Ford et al., 2009), and specially designed kindergarten curricula (Diamond, Barnett, Thomas, & Munro, 2007). Given mixed evidence regarding the impact of early intervention on the general cognitive and motor development of preterm/ELBW infants (Orton, Spittle, Doyle, Anderson, et al., 2009), it would be valuable in future research to evaluate the effectiveness of programs designed specifically to enhance children's EF. If, as already discussed, even normally intelligent preterm/ELBW children experience executive problems that injure their prospects at school, especially if they are from low SES families, then it would make sense to apply such programs broadly and not just to those children who are identified at a young age as having a learning disability.

Of course, future research should also be directed at increasing our understanding of the neurological bases of EF impairments, with a view to improving protocols of care for preterm/ELBW infants. Participants in the present study were largely unscathed by necrotizing enterocolitis (only one case), PVL (only one case), ventricular dilatation (only six mild cases), and CVH (seven mild and one moderate case), meaning that our risk score was derived from PDA, RDS, and indices of the need for breathing assistance; of these, all showed at least some negative relations with EF performance in their own right. The adverse consequences of PDA and RDS for brain development are well known (Volpe, 2008), and at least one previous study has observed that longer periods of ventilation support are associated with an enhanced likelihood of cognitive impairment, particularly in relation to self-regulation (e.g., Patrianakos-Hoobler et al., 2009). Without large-scale, multi-site designs, though, it is impossible to know whether EF impairments stem from the medical problem itself, the way it is managed, or some combination of both.

Conclusion

In conclusion, the present study demonstrated impairments of EF in a sample of 7–9-year-old children born extremely premature and/or with ELBW who were nevertheless making normal progress at school. Despite a modest sample size, we were able to detect reliable relations between biomedical variables (birth weight and neurobiological risk) and aspects of EF, such that the higher birth weights and lower levels of risk were associated with better performance. As discussed, the findings underscore the need for further study of the role of neurobiological risk factors in predicting the cognitive outcomes of preterm/ELBW birth. Finally, evidence that the negative impact of neurobiological risk was attenuated for participants from higher SES backgrounds is suggestive of a contribution of nurture to the development of EF. As such, the findings offer hope that effective interventions can be devised to enhance EF in preterm/ELBW children.

Funding

This study was funded by a research grant from the John P. Kelly Mater Research Foundation.

Conflict of Interest

None declared.

References

Aarnoudse-Moens
C. S. H.
Smidts
D. P.
Oosterlaan
J.
Duivenvoorden
H. J.
Weisglas-Kuperus
N.
Executive function in very preterm children at early school age
Journal of Abnormal Child Psychology
 , 
2009
, vol. 
37
 (pg. 
981
-
993
)
Aarnoudse-Moens
C. S. H.
Weisglas-Kuperus
N.
van Goudoever
J. B.
Oosterlaan
J.
Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children
Pediatrics
 , 
2009
, vol. 
124
 (pg. 
717
-
728
)
Abernethy
L. J.
Palaniappan
M.
Cooke
R. W.
Quantitative magnetic resonance imaging of the brain in survivors of very low birth weight
Archives of Diseases in Childhood
 , 
2002
, vol. 
87
 (pg. 
279
-
283
)
Anderson
P.
Assessment and development of executive function (EF) during childhood
Child Neuropsychology
 , 
2002
, vol. 
8
 
2
(pg. 
71
-
82
)
Anderson
P.
Doyle
L
for the Victorian Infant Collaborative Study Group
Neurobehavioral outcomes of school-age children who were born very preterm or with extremely low birth weight in the 1990s
Pediatrics
 , 
2003
, vol. 
114
 (pg. 
50
-
57
)
Anderson
P.
Howard
K.
Doyle
L.
Nosarti
C.
Murray
R.
Hack
M.
Executive function development in preterm children
Neurodevelopmental outcomes of preterm birth
 , 
2010
New York
Cambridge University Press
(pg. 
195
-
208
)
Anderson
P. J.
De Luca
C. R.
Hutchinson
E.
Spencer-Smith
M. M.
Roberts
G.
Doyle
L.W.
the Victorian Infant Collaborative Study Group
Attention problems in a representative sample of extremely preterm/extremely low birth weight children
Developmental Neuropsychology
 , 
2011
, vol. 
36
 
1
(pg. 
57
-
73
)
Anderson
P. J.
Doyle
L. W.
Cognitive and educational deficits in children born extremely preterm
Seminars in Perinatology
 , 
2008
, vol. 
32
 (pg. 
51
-
58
)
Aylward
G. P.
The relationship between environmental risk and developmental outcome
Developmental and Behavioral Pediatrics
 , 
1992
, vol. 
13
 
3
(pg. 
222
-
229
)
Bayless
S.
Stevenson
J.
Executive functions in school-age children born very prematurely
Early Human Development
 , 
2007
, vol. 
83
 (pg. 
247
-
254
)
Bernier
A.
Carlson
S. M.
Whipple
N.
From external regulation to self-regulation: Early parenting precursors of young children's executive functioning
Child Development
 , 
2010
, vol. 
81
 
1
(pg. 
326
-
339
)
Blair
C.
How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability
Behavioral and Brain Sciences
 , 
2006
, vol. 
29
 (pg. 
109
-
160
)
Bohm
B.
Smedler
A. C.
Forssberg
H.
Impulse control, working memory and other executive functions in preterm children when starting school
Acta Paediatrica (Oslo, Norway)
 , 
2004
, vol. 
93
 (pg. 
1363
-
1371
)
Briscoe
J.
Gathercole
S. E.
Marlow
N.
Everyday memory and cognitive ability in children born very prematurely
Journal of Child Psychology and Psychiatry
 , 
2001
, vol. 
4
 (pg. 
749
-
754
)
Carlson
S. M.
Executive function in context: Development, measurement, theory, and experience
Monographs of the society for research in child development
 , 
2003
, vol. 
68
 
3
(pg. 
138
-
151
Serial No. 274
Clark
C. A. C.
Pritchard
V. E.
Woodward
L. J.
Preschool executive functioning abilities predict early mathematics achievement
Developmental Psychology
 , 
2010
, vol. 
46
 
5
(pg. 
1176
-
1191
)
Curtis
W. J.
Lindeke
L. L.
Georgieff
M. K.
Nelson
C. A.
Neurobehavioral functioning in neonatal intensive care unit graduates in late childhood and early adolescence
Brain
 , 
2002
, vol. 
125
 (pg. 
1646
-
1659
)
Diamond
A.
Barnett
W. S.
Thomas
J.
Munro
S.
Preschool program improves cognitive control
Science
 , 
2007
, vol. 
318
 (pg. 
1387
-
1388
)
Downie
A. L. S.
Frisk
V.
Jakobson
L. S.
The impact of periventricular brain injury on reading and spelling abilities in the late elementary and adolescent years
Child Neuropsychology
 , 
2005
, vol. 
11
 
6
(pg. 
479
-
495
)
Dowsett
S.
Livesey
D. J.
The development of inhibitory control in pre-school children: Effects of “executive skills” training
Developmental Psychobiology
 , 
2000
, vol. 
36
 
2
(pg. 
161
-
174
)
Farah
M. J.
Betancourt
L.
Shera
D. M.
Savage
J. H.
Gianetta
J. M.
Brodsky
N. L.
, et al.  . 
Environmental stimulation, parental nurturance, and cognitive development in humans
Developmental Science
 , 
2008
, vol. 
11
 
5
(pg. 
793
-
801
)
Forcada-Guex
M.
Pierrehumbert
B.
Borghini
A.
Moessinger
A.
Muller-Nix
C.
Early dyadic patterns of mother-infant interactions and outcomes of prematurity at 18 months
Pediatrics
 , 
2006
, vol. 
118
 (pg. 
e107
-
114
)
Ford
R. M.
McDougall
S. J. P.
Evans
D.
Parent-delivered compensatory education for children at risk of educational failure: Improving the academic and self-regulatory skills of a Sure Start preschool sample
British Journal of Psychology
 , 
2009
, vol. 
100
 (pg. 
773
-
797
)
Frye
R. E.
Malmberg
B.
Swank
P.
Smith
K.
Landry
S.
Preterm birth and maternal responsiveness during childhood are associated with brain morphology in adolescence
Journal of the International Neuropsychological Society
 , 
2010
, vol. 
16
 (pg. 
784
-
794
)
Gardner
J. M.
Karmel
B. Z.
Magano
C. L.
Norton
K. I.
Brown
E. G.
Neurobehavioral indicators of early brain insult in high-risk neonates
Developmental Psychology
 , 
1990
, vol. 
26
 (pg. 
563
-
575
)
Golden
C. J.
Stroop color and word test
 , 
1978
Chicago
Stoelting
Harvey
J. M.
O'Callaghan
M. J.
Mohay
H.
Executive function of children with extremely low birthweight: A case control study
Developmental Medicine and Child Neurology
 , 
1999
, vol. 
41
 (pg. 
292
-
297
)
Isaacs
E. B.
Lucas
A.
Chong
W. K.
Wood
S. J.
Johnson
C. L.
Marshall
C.
, et al.  . 
Hippocampal volume and everyday memory in children of very low birthweight
Pediatric Research
 , 
2000
, vol. 
47
 (pg. 
713
-
720
)
Jarman
R. F.
Vavrik
J.
Walton
P. D.
Metacognitive and frontal lobe processes: At the interface of cognitive psychology and neuropsychology
Genetic, Social and General Psychology Monographs
 , 
1995
, vol. 
121
 
2
(pg. 
155
-
210
)
Johnson
S.
Wolke
D.
Hennessy
E.
Marlow
N.
Educational outcomes in extremely preterm children: neuropsychological correlates and predictors of attainment
Developmental Neuropsychology
 , 
2011
, vol. 
36
 
1
(pg. 
74
-
95
)
Kaplan
E.
Fein
D.
Kramer
J.
Delis
D.
Morris
R.
WISC-II as a process instrument manual
 , 
1999
San Antonio, TX
The Psychological Corporation
Kelly
Y. J.
Nazroo
J. Y.
McMunn
A.
Boreham
R.
Marmot
M.
Birthweight and behavioral problems in children: A modifiable effect?
International Journal of Epidemiology
 , 
2001
, vol. 
30
 (pg. 
88
-
94
)
Landry
S. H.
Denson
S. E.
Swank
P. R.
Effects of medical risk and socioeconomic status on the rate of change in cognitive and social development for low birth weight children
Journal of Clinical and Experimental Neuropsychology
 , 
1997
, vol. 
19
 
2
(pg. 
261
-
274
)
Landry
S. H.
Miller-Loncar
C. L.
Smith
K. E.
Swank
P. R.
The role of early parenting in children's development of executive processes
Developmental Neuropsychology
 , 
2002
, vol. 
21
 (pg. 
15
-
41
)
Landry
S. H.
Smith
K. E.
Swank
P. R.
Assel
M. A.
Vellett
S.
Does early responsive parenting have a special important for children's development or is consistency across early childhood necessary?
Developmental Psychology
 , 
2001
, vol. 
37
 (pg. 
387
-
403
)
Leh
S. H.
Petrides
M.
Strafella
A. P.
The neural circuitry of executive functions in healthy subjects and Parkinson's disease
Neuropsychopharmacology
 , 
2010
, vol. 
35
 (pg. 
70
-
85
)
Luciana
M.
Cognitive development in children born preterm: Implications for theories of brain plasticity following early injury
Development and Psychopathology
 , 
2003
, vol. 
15
 
4
(pg. 
1017
-
1047
)
Marlow
N.
Hennessy
E. M.
Bracewell
M. A.
Wolke
D.
Motor and executive function at 6 years of age after extremely preterm birth
Pediatrics
 , 
2007
, vol. 
120
 
4
(pg. 
793
-
804
)
Milgrom
J.
Newnham
C.
Anderson
P. J.
Doyle
L. W.
Gemmill
A. W.
Lee
K.
, et al.  . 
Early sensitivity training for parents of preterm infants: Impact on the developing brain
Pediatric Research
 , 
2010
, vol. 
67
 
3
(pg. 
330
-
335
)
Mulder
H.
Pitchford
N. J.
Hagger
M. S.
Marlow
N.
Development of executive function and attention in preterm children: A systematic review
Developmental Neuropsychology
 , 
2009
, vol. 
34
 
4
(pg. 
393
-
421
)
Narberhaus
A.
Segarra
D.
Caldú
X.
Giménez
M.
Pueyo
R.
Botet
F.
, et al.  . 
Corpus callosum and prefrontal functions in adolescents with history of very preterm birth
Neuropsychologia
 , 
2008
, vol. 
46
 (pg. 
111
-
116
)
Narberhaus
A.
Segarra
D.
Giménez
M.
Junqué
C.
Pueyo
R.
Botet
F.
Memory performance in a sample of very low birth weight adolescents
Developmental Neuropsychology
 , 
2007
, vol. 
31
 (pg. 
129
-
135
)
Noble
K. G.
Norman
M. F.
Farah
M. J.
Neurocognitive correlates of socio-economic status in kindergarten children
Developmental Science
 , 
2005
, vol. 
8
 (pg. 
74
-
87
)
Nosarti
C.
Giouroukou
E.
Micali
N.
Rifkin
L.
Morris
R. G.
Murray
R. M.
Impaired executive functioning in young adults born very preterm
Journal of the International Neuropsychological Society
 , 
2007
, vol. 
13
 (pg. 
571
-
581
)
Nosarti
C.
Rushe
T. M.
Woodruff
P. W. R.
Stewart
A. L.
Rifkin
L.
Murray
R. M.
Corpus callosum size and very preterm birth: Relationship to neuropsychological outcome
Brain
 , 
2004
, vol. 
127
 (pg. 
2080
-
2089
)
O'Donnell
P.
Grace
A. A.
Synaptic interactions among excitatory afferents to nucleus accumbens neurons: Hippocampal gating of prefrontal cortical input
Journal of Neuroscience
 , 
1995
, vol. 
15
 (pg. 
3622
-
3639
)
Orton
J.
Spittle
A.
Doyle
L.
Anderson
P.
Boyd
R.
Do early intervention programmes improve cognitive and motor outcomes for preterm infants after discharge? A systematic review
Developmental Medicine and Child Neurology
 , 
2009
, vol. 
51
 
11
(pg. 
851
-
859
)
Patrianakos-Hoobler
A. I.
Msall
M. E.
Marks
J. D.
Huo
D.
Schreiber
M. D.
Risk factors affecting school readiness in premature infants with respiratory distress syndrome
Pediatrics
 , 
2009
, vol. 
124
 (pg. 
258
-
267
)
Reitan
R. M.
Wolfson
D.
The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation
 , 
1993
Tucson, AZ
Neuropsychology Press
Rimm-Kaufman
S.
Pianta
R. C.
Cox
M.
Teachers' judgments of problems in the transition to kindergarten
Early Childhood Research Quarterly
 , 
2000
, vol. 
15
 (pg. 
147
-
166
)
Roberts
G.
Bellinger
D.
McCormick
M. C.
A cumulative risk factor model for early identification of academic difficulties in premature and low birth weight infants
Maternal and Child Health Journal
 , 
2007
, vol. 
11
 (pg. 
161
-
172
)
Robson
A. L.
Pederson
D. R.
Predictors of individual differences in attention among low birth weight children
Developmental and Behavioral Pediatrics
 , 
1997
, vol. 
18
 
1
(pg. 
13
-
21
)
Rueda
M. R.
Rothbart
M. K.
McCandliss
B. D.
Saccomanno
L.
Posner
M. I.
Training, maturation, and genetic influences on the development of executive attention
Proceedings of the National Academy of Sciences of the USA
 , 
2005
, vol. 
102
 (pg. 
14931
-
14936
)
Rushe
T. M.
Rifkin
L.
Stewart
A. L.
Townsend
J. P.
Roth
S. C.
Wyatt
J. S.
, et al.  . 
Neuropsychological outcome at adolescence of very preterm birth and its relation to brain structure
Developmental Medicine and Child Neurology
 , 
2001
, vol. 
43
 (pg. 
226
-
233
)
Schroeder
V.
Kelley
M. L.
Associations between family environment, parenting practices, and executive functioning of children with and without ADHD
Journal of Child and Family Studies
 , 
2009
, vol. 
18
 (pg. 
227
-
237
)
Shum
D.
Gill
H.
Banks
M.
Maujean
A.
Griffin
J.
Ward
H.
Planning ability following moderate to severe traumatic brain injury: Performance on a 4-disk version of the Tower of London
Brain Impairment
 , 
2009
, vol. 
10
 (pg. 
320
-
324
)
Shum
D.
Neulinger
K.
O'Callaghan
M.
Mohay
H.
Attentional problems in children born very preterm or with extremely low birth weight at 7–9 years
Archives of Clinical Neuropsychology
 , 
2008
, vol. 
23
 (pg. 
103
-
112
)
Spreen
O.
Strauss
E.
A compendium of neuropsychological tests: Administration, norms and commentary
 , 
1998
2nd ed.
New York
Oxford University Press
St Clair-Thompson
H. L.
Gathercole
S. E.
Executive functions and achievements in school: Shifting, updating, inhibition, and working memory
The Quarterly Journal of Experimental Psychology
 , 
2006
, vol. 
59
 
4
(pg. 
745
-
759
)
Taylor
H. G.
Klein
N.
Minich
N. M.
Hack
M.
Middle-school-age outcomes in children with very low birthweight
Child Development
 , 
2000
, vol. 
71
 (pg. 
1495
-
1511
)
Taylor
H. G.
Klein
N.
Schatschneider
C.
Hack
M.
Predictors of early school age outcomes in very low birth weight children
Journal of Developmental and Behavioral Pediatrics
 , 
1998
, vol. 
19
 (pg. 
235
-
243
)
Thatcher
R. W.
Maturation of the human frontal lobes: Physiological evidence for staging
Developmental Neuropsychology
 , 
1991
, vol. 
7
 (pg. 
397
-
419
)
Vicari
S.
Caravale
B.
Carlesimo
G. A.
Casedi
A. M.
Allemand
F.
Spatial working memory deficits in children at ages 3–4 who were low birth weight, preterm infants
Neuropsychology
 , 
2004
, vol. 
18
 (pg. 
673
-
678
)
Visu-Petra
L.
Benga
O.
Miclea
M.
Dimensions of attention and executive functioning in 5- to 12-years-old children: Neuropsychological assessment with the NEPSY battery
Cognition, Brain, Behavior
 , 
2007
, vol. 
XI
 
3
(pg. 
585
-
608
)
Volpe
J. J.
Neurology of the newborn
 , 
2008
5th ed.
Philadelphia, PA
Saunders Elsevier
Vygotsky
L. S.
Mind in society: The development of higher psychological processes
 , 
1978
Cambridge, MA
Harvard University Press
Wechsler
D.
Wechsler Intelligence Scale for Children
 , 
1991
3rd ed.
San Antonio, TX
The Psychological Corporation
Weindrich
D.
Jennen-Steinmetz
C.
Laucht
M.
Schmidt
M. H.
Late sequelae of low birthweight: Mediators of poor school performance at 11 years
Developmental Medicine and Child Neurology
 , 
2003
, vol. 
45
 
7
(pg. 
463
-
469
)
Welsh
J. A.
Nix
R. L.
Blair
C.
Bierman
K. L.
Nelson
K. E.
The development of cognitive skills and gains in academic school readiness for children from low-income families
Journal of Educational Psychology
 , 
2010
, vol. 
102
 
1
(pg. 
43
-
53
)
Wood
N. S.
Marlow
N.
Costeloe
K.
Gibson
A. T.
Wilkinson
A. R.
Neurologic and developmental disability after extremely preterm birth. EPICure Study Groups
The New England Journal of Medicine
 , 
2000
, vol. 
343
 (pg. 
378
-
384
)