Correction
10 Sep 2015: Chaddock-Heyman L, Erickson KI, Kienzler C, King M, Pontifex MB, et al. (2015) Correction: The Role of Aerobic Fitness in Cortical Thickness and Mathematics Achievement in Preadolescent Children. PLOS ONE 10(9): e0138166. https://doi.org/10.1371/journal.pone.0138166 View correction
Figures
Abstract
Growing evidence suggests that aerobic fitness benefits the brain and cognition during childhood. The present study is the first to explore cortical brain structure of higher fit and lower fit 9- and 10-year-old children, and how aerobic fitness and cortical thickness relate to academic achievement. We demonstrate that higher fit children (>70th percentile VO2max) showed decreased gray matter thickness in superior frontal cortex, superior temporal areas, and lateral occipital cortex, coupled with better mathematics achievement, compared to lower fit children (<30th percentile VO2max). Furthermore, cortical gray matter thinning in anterior and superior frontal areas was associated with superior arithmetic performance. Together, these data add to our knowledge of the biological markers of school achievement, particularly mathematics achievement, and raise the possibility that individual differences in aerobic fitness play an important role in cortical gray matter thinning during brain maturation. The establishment of predictors of academic performance is key to helping educators focus on interventions to maximize learning and success across the lifespan.
Citation: Chaddock-Heyman L, Erickson KI, Kienzler C, King M, Pontifex MB, Raine LB, et al. (2015) The Role of Aerobic Fitness in Cortical Thickness and Mathematics Achievement in Preadolescent Children. PLoS ONE 10(8): e0134115. https://doi.org/10.1371/journal.pone.0134115
Editor: Maurice Ptito, University of Montreal, CANADA
Received: April 8, 2015; Accepted: July 6, 2015; Published: August 12, 2015
Copyright: © 2015 Chaddock-Heyman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: Funding was provided by grants from the National Institute on Aging at the National Institute of Health to Arthur Kramer (RO1 AG25032 and R37 AG025667) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD055352) to Charles Hillman. Kirk Erickson was supported by the National Institutes of Diabetes and Digestive and Kidney Diseases (R01 DK095172). Matthew Pontifex was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21 HD078566). Lauren Raine was supported by the National Institute for Agriculture under the Illinois Transdisciplinary Obesity Prevention Program grant (2011-67001-30101) to the Division of Nutritional Sciences at the University of Illinois.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Aerobic fitness and physical activity are beneficial to cognitive and brain health during development (see [1] for a review). Higher levels of aerobic fitness during childhood are associated with superior cognitive control, memory [2–8], and academic achievement [9,10]. Growing evidence suggests that these aerobic fitness differences in cognition and academics have a biological basis in the brain. In particular, higher fit children have larger structural brain volumes in the hippocampus and dorsal striatum, two subcortical regions critical for memory and learning [3,4], as well as more efficient brain activation patterns (via functional magnetic resonance imaging [fMRI] and event-related potential [ERP] measures) during attentional and interference control tasks [11–12; 5–8], relative to lower fit peers.
It is possible that aerobic fitness during childhood also influences the structure of cortical systems (as found in older adults, e.g., [13–14]), which may play a role in cognition and school performance. Cortical structure can be measured by determining cortical thickness, calculated by generating models of gray/white matter boundaries and pial surfaces, and calculating the distance between these two surfaces [15–18]. Normative maturation of cortical thickness provides a context to formulate predictions about how aerobic fitness might influence this brain measure in children. One longitudinal study scanned 45 children every 2 years from age 5 to 11 and demonstrated cortical thinning with development in dorsolateral frontal cortex, occipital-parietal areas, and anterior and posterior/inferior temporal regions (with rates of loss of approximately 0.1–0.3 mm per year) [19]. Furthermore, cortical thinning in the dorsal frontal and parietal regions was correlated with improved performance on a test of verbal intellectual functioning (vocabulary test of the Wechsler Intelligence Scale) [19]. In fact, research suggests that gray matter loss occurs as part of the sculpting of the brain into the fully functioning adult nervous system [19–20].
Higher levels of aerobic fitness are also known to predict better academic performance (e.g., mathematics, reading, English) during childhood [9–10; 21–22], and significant improvements in scholastic performance are associated with increased participation in physical activity during the school day [23–24]. However, little is known about the neural markers for academic success. In terms of neuroelectric indices, the P3 ERP component, reflective of attentional processes involved in stimulus evaluation and inhibition, has been suggested as a marker of reading and arithmetic achievement during childhood [25]. Here, we are the first to examine whether brain structural differences in higher and lower fit children relate to academic achievement. Understanding predictors of academic success, such as aerobic fitness and brain structure, has important implications, as standardized test performance can determine funding and effectiveness of educational programs as well as forecast a student's future scholastic success [26–27].
Given evidence that aerobic fitness is associated with specific measures of brain health and cognition during child development, we predicted that individual differences in aerobic fitness would be associated with cortical thickness, which would in turn be related to academic performance. Specifically, because cortical thinning is associated with brain development and maturation, we predicted that higher fit 9- and 10-year-old children would show decreased cortical thickness across the cortex, which would relate to better performance on the Wide Range Achievement Test (WRAT-3) of reading, spelling, and arithmetic achievement, relative to lower fit children.
Materials and Methods
Participants
Our study was reviewed and approved by the Institutional Review Board of the University of Illinois at Urbana-Champaign. Preadolescent 9- and 10-year-old children were recruited from East-Central Illinois. Children were screened for several factors that influence physical activity participation and cognitive function. The Kaufman Brief Intelligence Test (K-BIT) [28] was administered to each child to obtain a composite intelligence quotient (IQ) score including both crystallized and fluid intelligence measures. Participants were excluded if their scores were more than 1 standard deviation below the mean (85%). A guardian of the child also completed the Attention-Deficit Hyperactivity Disorder (ADHD) Rating Scale IV [29] to screen for the presence of attentional disorders. Participants were excluded if they scored above the 85th percentile. Pubertal timing was also assessed using a modified Tanner Staging System [30] with all participants at or below a score of 2 on a 5-point scale of developmental stages. In addition, socioeconomic status (SES) was determined by creating a trichotomous index based on three variables: participation in a free or reduced-price meal program at school, the highest level of education obtained by the child’s mother and father, and the number of parents who worked full-time [31].
Furthermore, eligible participants were required to (1) qualify as higher fit or lower fit (see Aerobic Fitness Assessment section), (2) demonstrate right handedness (as measured by the Edinburgh Handedness Questionnaire) [32], (3) report no adverse health conditions, physical incapacities, or neurological disorders, (4) report no use of medications that influenced central nervous system function, (5) successfully complete a mock Magnetic Resonance Imaging (MRI) session to screen for claustrophobia in an MRI machine, and (6) sign an informed assent approved by the University of Illinois at Urbana-Champaign. A legal guardian also provided written informed consent in accordance with the Institutional Review Board of the University of Illinois at Urbana-Champaign.
Forty-eight children were included in the analysis, including 24 higher fit participants (14 boys, 10 girls) and 24 lower fit participants (8 boys, 16 girls). Fifty-two children were eligible for the study and completed an MRI scan, and four children were excluded from analysis due to inaccurate gray-white tissue segmentation and motion noise in the reconstructed structural image.
Aerobic Fitness Assessment
The aerobic fitness level of each child was determined by measuring maximal oxygen uptake (VO2max) using a computerized indirect calorimetry system (ParvoMedics True Max 2400) during a modified Balke protocol [33]. Specifically, participants ran on a motor-driven treadmill at a constant speed with increases in grade increments of 2.5% every 2 minutes until volitional exhaustion. Averages for oxygen uptake (VO2) and respiratory exchange ratio (RER; the ratio between carbon dioxide and oxygen) were assessed every 20 seconds. In addition, heart rate was measured throughout the fitness test (using a Polar heart rate monitor [Polar WearLink + 31, Polar Electro, Finland]), and ratings of perceived exertion were assessed every 2 minutes using the children’s OMNI scale [34].
VO2max was defined when oxygen consumption remained at a steady state despite an increase in workload. Relative peak oxygen consumption was based upon maximal effort as evidenced by (1) a plateau in oxygen consumption corresponding to an increase of less than 2 mL/kg/min despite an increase in workload, (2) a peak heart rate greater than 185 beats per minute [33] accompanied by a heart rate plateau (i.e., an increase in work rate without a concomitant increase in heart rate) [35], (3) RER greater than 1.0 [36], and/or (4) ratings on the children’s OMNI scale of perceived exertion greater than 8 [34]. Relative peak oxygen consumption was expressed in mL/kg/min.
Aerobic fitness group assignments (i.e., higher fit and lower fit) were based on whether a child’s VO2max value fell above the 70th percentile (for age and gender) or below the 30th percentile (for age and gender) according to normative data provided by Shvartz and Reibold [37]. Children who did not qualify as higher fit or lower fit were excluded. All participants were compensated $10/hour for the demographic and VO2max protocol and $20 for participation in the MRI session.
MR Imaging Protocol and Cortical Thickness Analysis
For all participants, high-resolution (1.3 mm×1.3 mm×1.3 mm) T1- weighted structural brain images were acquired using a 3D MPRAGE (Magnetization Prepared Rapid Gradient Echo Imaging) protocol with 144 contiguous axial slices, collected in ascending fashion parallel to the anterior and posterior commissures (echo time = 3.87ms, repetition time = 1800ms, field of view = 256mm, acquisition matrix 192mm×192mm, slice thickness = 1.3mm, and flip angle = 8°). All images were collected on a 3-T head-only Siemens Allegra MRI scanner.
Automated brain tissue segmentation and reconstruction of cortical surface models were performed on T1-weighted structural MRI images using the standard recon-all image processing pipeline in FreeSurfer, version 5.2.0 (Released May, 2013; http://surfer-nmr.mgh.harvard.edu/). FreeSurfer automatically labels cortical surfaces using a Desikan-Killiany cortical parcellation atlas (see 38 for the labeling protocol). That is, vertices along the cortical surface are assigned a given label based on local surface curvature, average convexity, prior label probabilities, and neighboring vertex labels [38–39]. Data from all participants were processed using the same Apple OSX 10.8 computer to ensure that the observed findings were not a function of differences in software, operating system, or hardware specifications [40].
Specifically, the following processing stream was applied to each participant’s structural image via FreeSurfer’s recon-all processing pipeline: (1) non-brain tissue removal, (2) Talairach transformation, (3) creation of representations of the gray/white matter boundaries [41–42], and (4) calculation of the cortical thickness as the distance between the gray/white matter boundary and the pial surface in all regions of interest [15]. Our a priori regions of interest included frontal (anterior, middle, superior), parietal (superior, inferior), temporal (superior, middle, inferior), and lateral occipital regions, as offered in FreeSurfer’s segmentation algorithms (Fig 1, [43]). These areas provide an exploratory analysis of the whole-brain and include regions of interest found to change with development [19]. Talairach transforms, skull stripping, gray–white tissue segmentation, and surface reconstructions were visually checked for errors (and, as noted, four children were excluded from analysis due to inaccurate gray-white tissue segmentation and motion noise in the reconstructed structural image).
Starred regions are areas in which higher fit children showed decreased cortical thickness compared to lower fit children.
Wide Range Achievement Test (WRAT-3)
Academic achievement was assessed using the paper and pencil WRAT – 3rd edition (Wide Range, Inc., Wilmington, DE). The test battery included the content areas of reading (i.e., the number of words pronounced aloud correctly), spelling (i.e., the number of words spelled correctly), and arithmetic (i.e., the number of mathematical computations completed correctly). The WRAT-3 has been strongly correlated with the California Achievement Test–Form E and the Stanford Achievement Test [44].
Statistical Analyses
Given the recruitment of higher and lower aerobic fitness groups, independent t-tests were conducted to compare demographic and fitness measures. We then performed a multivariate analysis of variance (MANOVA) to examine associations between aerobic fitness group (higher fit, lower fit) and cortical thickness in all areas of interest, across left and right hemispheres (Fig 1). Given a significant multivariate effect, secondary univariate ANOVAs were conducted to examine differences in cortical thickness between higher fit and lower fit children. Left and right thickness measures were averaged due to significant correlations between left and right thickness (all r>0.31, p<0.03) and no primary hypotheses about hemispheric differences as a function of aerobic fitness.
Additionally, independent t-tests were employed to compare WRAT-3 scores in higher fit and lower fit children. Pearson correlations were also conducted to determine associations between cortical thickness and academic achievement. The alpha level for all tests was set at p < .05.
Results
Participant demographic and fitness data are provided in Table 1. Demographic variables (i.e., age, gender, IQ, ADHD, pubertal timing, SES) did not differ between fitness groups. Furthermore, consistent with our recruitment of extreme aerobic fitness groups, higher fit participants (M = 52.6 mL/kg/min, SD = 4.8 mL/kg/min) had higher VO2max than lower fit children (M = 35.7 mL/kg/min, SD = 5.2 mL/kg/min) as revealed by an independent t-test (t (46) = 11.8, p<0.001).
The overall multivariate test indicated a significant effect of aerobic fitness on cortical thickness (F (22, 27) = 2.41, p = 0.017). Next, univariate ANOVAs were performed to identify the specific cortical regions that contributed to the overall effect. Higher fit children showed decreased cortical thickness in superior frontal cortex (F (1, 46) = 4.80, p = 0.034), superior temporal cortex (F (1, 46) = 5.39, p = 0.025) and lateral occipital cortex (F (1, 46) = 5.67, p = 0.021), relative to lower fit children (Table 2). There was also specificity to the aerobic fitness differences, with some brain areas not showing aerobic fitness group differences in thickness, including the anterior frontal cortex (F (1, 46) = 2.33, p = 0.13), middle frontal cortex (F (1, 46) = 1.98, p = 0.17), middle temporal cortex F (1, 46) = 0.54, p = 0.47), inferior temporal cortex (F (1, 46) = 2.46, p = 0.12), superior parietal cortex (F (1, 46) = 1.21, p = 0.28), and inferior parietal areas (F (1, 46) = 0.63, p = 0.43) (Table 2). All cortical thickness values and effect sizes (Cohen’s d) are provided in Table 2.
Behaviorally, higher fit children showed superior mathematics achievement compared to lower fit children (t (46) = 1.98, p = 0.05) on the WRAT-3. No fitness differences were found for reading or spelling performance (t< 1.1, p>0.3). In addition, across all children, WRAT-3 arithmetic scores were negatively correlated with cortical thickness in anterior frontal cortex (r = -0.292, p = 0.04), and superior frontal cortex (r = -0.291, p = 0.04) (Table 3) (Fig 2).
Discussion
Consistent with predictions, our results demonstrate that higher fit 9- and 10-year-old children (>70th percentile VO2max) showed decreased gray matter thickness in superior frontal cortex, superior temporal areas, and lateral occipital cortex, coupled with better arithmetic performance on a standardized achievement test, compared to lower fit children (<30th percentile VO2max). Furthermore, cortical gray matter thinning in anterior and superior frontal areas was associated with superior mathematics achievement. Together, our data raise the possibility that individual differences in aerobic fitness play a role in childhood cortical gray matter structure important for scholastic success, particularly on mathematics tests. These novel results add to our understanding of developmental plasticity in brain and cognition as a function of aerobic fitness, as well as the neural correlates of performance on measures of importance in education.
Our results support and extend research on changes in cortical surface organization during child development [19] and across the lifespan [20]. That is, the brain areas that showed cortical thinning as a function of higher levels of aerobic fitness (i.e., frontal, temporal and occipital regions) are similar to developing brain areas that undergo significant cortical thinning between the ages of 5 and 11 (i.e., lateral frontal cortex, temporal regions, and occipital areas) [19]. Moreover, areas of the dorsal frontal cortex and superior frontal sulcus have demonstrated non-linear declines in gray matter density with age across the lifespan (age 7–60) [20]. Here, our data raise the possibility that individual differences in aerobic fitness may influence some areas that show significant changes in cortical thickness during development, and perhaps even across the lifespan. Although the methods for segmentation of cortical regions and calculation of gray matter thickness/density are not identical here and in previous work [19–20], it is possible that aerobic fitness is one predictor of the developmental trajectory of cortical structure in certain areas. It is important to note that we also demonstrate some specificity of the effects, as aerobic fitness was not associated with significant gray matter thickness differences in areas such as middle frontal, middle temporal and superior and inferior parietal cortex. Future work should employ whole-brain analyses corrected for multiple comparisons to continue to explore the specific effects of aerobic fitness on cortical brain structure during childhood.
We also found that decreased cortical gray matter thickness in anterior and superior frontal cortex predicted better performance on a paper and pencil test of mathematics achievement known to correlate with standardized achievement assessments in the classroom [44]. The results add to the biological markers for academic success [25], and also raise the possibility that fitness-related differences in cortical structure (in particular, the superior frontal cortex) have important scholastic implications (in particular, for mathematics achievement). Successful mathematics problem solving is said to involve working memory, the ability to hold relevant information in mind for efficient and effective comprehension [45–47] as well as inhibition, the ability to ignore irrelevant information [48]. Higher fit children have shown superior performance on cognitive control tasks that challenge working memory and inhibitory control [11–12, 5–6, 8], as well as superior performance on standardized tests of mathematics and reading [9–10], relative to lower fit children. Together, our study suggests that differences in cortical gray matter structure in frontal cortex may predict superior arithmetic performance in school, and aerobic fitness may be one pathway by which brain and cognition are enhanced during development.
Furthermore, it is interesting to note that we suggest a unique association among aerobic fitness, cortical thickness and specifically arithmetic achievement, rather than global scholastic success across reading, spelling and arithmetic. Future efforts should be directed toward determining additional neural biomarkers for scholastic success, and whether these biomarkers predict performance on select academic subjects, as suggested here, or whether they serve as a more global index of overall school performance. For instance, the P3 is suggested to be a marker of both reading and arithmetic performance during childhood [25], and aerobic fitness has been found to relate to both English/reading and mathematics achievement [9,10]. Interestingly, another study showed specific effects of aerobic exercise training on mathematics achievement, with no benefit to reading, in a sample of 7- to 11-year-old children [49]. Through additional research, we will better understand how both lifestyle factors and neural and cognitive processes account for unique variance in scholastic success, known to forecast future success [26,27]. Mediation models among aerobic fitness, cortical structure, and scholastic success should be tested with larger sample sizes and randomized physical activity interventions to examine whether differences in cortical thickness represent a potential causal pathway between physical activity, fitness, and elevated cognitive and scholastic performance.
The present study arrives at an important time. Physical activity opportunities during the school day are being reduced or eliminated in response to mandates for increased academic classroom time [50], and rising rates of physical inactivity [51–52]. Here we provide additional evidence that increased aerobic fitness levels may enhance cognitive and brain plasticity, with potentially significant outcomes related to scholastic achievement. Additionally, we suggest neural predictors of academic performance. Understanding individual differences in brain health and academic performance has significant implications for educators and policy makers who aim to determine strategies and interventions to maximize learning and success across the lifespan.
Supporting Information
S1 Dataset. Aerobic fitness and cortical thickness data for 48 participants.
https://doi.org/10.1371/journal.pone.0134115.s001
(SAV)
Acknowledgments
Funding was provided by grants from the National Institute on Aging at the National Institute of Health to Arthur Kramer (RO1 AG25032 and R37 AG025667) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD055352) to Charles Hillman. Kirk Erickson was supported by the National Institutes of Diabetes and Digestive and Kidney Diseases (R01 DK095172). Matthew Pontifex was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21 HD078566). Lauren Raine was supported by the National Institute for Agriculture under the Illinois Transdisciplinary Obesity Prevention Program grant (2011-67001-30101) to the Division of Nutritional Sciences at the University of Illinois. We extend our appreciation to Holly Tracy and Nancy Dodge for their help with data collection as well as Nate Medeiros-Ward and Michael Kranz for their input about the Freesurfer analysis.
Author Contributions
Conceived and designed the experiments: LCH KIE MP CH LR AK. Performed the experiments: LCH MP LR CH AK. Analyzed the data: LCH MP LR. Contributed reagents/materials/analysis tools: LCH KIE CK MK MP LR CH AK. Wrote the paper: LCH KIE CH AK.
References
- 1. Chaddock L, Pontifex MB, Hillman CH, & Kramer AF. A review of the relation of aerobic fitness and physical activity to brain structure and function in children. J Int Neuropsychol Soc. 2011;17: 1–11.
- 2. Buck SM, Hillman CH, & Castelli DM. The relation of aerobic fitness to stroop task performance in preadolescent children. Med Sci Sports Exerc. 2008;40: 166–172. pmid:18091008
- 3. Chaddock L, Erickson KI, Prakash RS, Kim JS, Voss MW, VanPatter M, et al. A neuroimaging investigation of the association between aerobic fitness, hippocampal volume and memory performance in preadolescent children. Brain Res. 2010a;1358: 172–183.
- 4. Chaddock L, Erickson KI, Prakash RS, VanPatter M, Voss MV, Pontifex MB, et al. Basal ganglia volume is associated with aerobic fitness in preadolescent children. Dev Neurosci. 2010b;32: 249–256.
- 5. Hillman CH, Pontifex MB, Castelli DM, Khan NA, Raine LB, Scudder MR et al. (2014). Effects of the FITKids randomized controlled trial on executive control and brain function. Pediatrics. 2014;134: 1063–1071.
- 6. Pontifex MB, Raine LB, Johnson CR, Chaddock L, Voss MW, Cohen NJ, et al. Cardiorespiratory fitness and the flexible modulation of cognitive control in preadolescent children. J Cogn Neurosci. 2011;23: 1332–1345. pmid:20521857
- 7. Tomporowski PD, Davis CL, Miller PH, & Naglieri JA. Exercise and children’s intelligence, cognition, and academic achievement. Educ Psychol Rev. 2008;20: 111–31. pmid:19777141
- 8. Voss MW, Chaddock L, Kim JS, VanPatter M, Pontifex MB, Raine LB, et al. Aerobic fitness is associated with greater efficiency of the network underlying cognitive control in preadolescent children. Neuroscience. 2011;199: 166–76. pmid:22027235
- 9. Castelli DM, Hillman CH, Buck SM, & Erwin HE. Physical fitness and academic achievement in third- and fifth-grade students. J Sport Exerc Psychol. 2007;29: 239–252. pmid:17568069
- 10. Chomitz VR, Slining MM, McGowan RJ, Mitchell SE, Dawson GF, & Hacker KA. Is there a relationship between physical fitness and academic achievement?: Positive results from public school children in the northeastern United States. J Sch Health. 2009;79: 30–37. pmid:19149783
- 11. Chaddock L, Erickson KI, Prakash RS, Voss MV, VanPatter M, Pontifex MB, et al. A functional MRI investigation of the association between childhood aerobic fitness and neurocognitive control. Biol Psychol. 2012;89: 260–268. pmid:22061423
- 12. Chaddock-Heyman L, Erickson KI, Voss MW, Knecht AM, Pontifex MB, Castelli DM, et al. The effects of physical activity on functional MRI activation associated with cognitive control in children: a randomized controlled intervention. Front Hum Neurosci. 2013;7: 1–13.
- 13. Colcombe SJ, Erickson KI, Scalf PE, Kim JS, Prakash R, McAuley E, et al. Aerobic exercise training increases brain volume in aging humans. J Gerontol: Med Sci. 2006;61: 1166–1170.
- 14. Erickson KI, Raji CA, Lopez OL, Becker JT, Rosano C, Newman AB, et al. Physical activity predicts gray matter volume in late adulthood: The Cardiovascular Health Study. Neurology. 2010;75: 1415–1422. pmid:20944075
- 15. Fischl B, & Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A. 2000;97: 11050–11055. pmid:10984517
- 16. Jones SE, Buchbinder BR, & Aharon I. Three-dimensional mapping of cortical thickness using Laplace’s equation. Hum Brain Mapp. 2000;11: 12–32. pmid:10997850
- 17. Kabani N, Le GG, MacDonald D, & Evans AC. (2001) Measurement of cortical thickness using an automated 3-D algorithm: A validation study. Neuroimage. 2001;13: 375–380. pmid:11162277
- 18. MacDonald D, Kabani N, Avis D, & Evans AC. Automated 3- D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage. 2000;12: 340–356. pmid:10944416
- 19. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, & Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci. 2004;24: 8223–31. pmid:15385605
- 20. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, & Toga AW. Mapping cortical change across the human life span. Nat Neurosci. 2006;6: 309–315.
- 21. Grissom J. Physical fitness and academic achievement. J Exerc Physiol. 2005;8: 11–25.
- 22. Sibley BA, & Etnier JL. The relationship between physical activity and cognition in children: A meta-analysis. Pediatr Exerc Sci. 2003;15: 243–256.
- 23. Coe DP, Ivarnik JM, Womack CJ, Reeves MJ, & Malina RM. Effects of physical education and physical activity levels on academic achievement in children. Med Sci Sports Exerc. 2006;38: 1515–1519. pmid:16888468
- 24. Donnelly JE, Greene JL, Gibson CA, Smith BK, Washburn RA, Sullivan DK, et al. Physical Activity Across the Curriculum (PAAC): A randomized controlled trial to promote physical activity and diminish overweight and obesity in elementary school children. Prev Med. 2009;49: 336–341. pmid:19665037
- 25. Hillman CH, Pontifex MB, Motl RW, O'Leary KC, Johnson CR, Scudder MR, et al. From ERP's to academics. Dev Cogn Neurosci. 2012;2S: S90–S98.
- 26. Kuncel NR, & Hezlett SA. Standardized tests predict graduate students’ success. Science. 2007;315: 1080–1081. pmid:17322046
- 27. Kuncel NR, Hezlett SA, & Ones DS. Academic performance, career potential, creativity, and job performance: can one construct predict them all? J Pers Soc Psychol. 2004;86: 148–161. pmid:14717633
- 28.
Kaufman AS, & Kaufman NL. Kaufman Brief Intelligence Test. Circle Pines, MN: AGS; 1990.
- 29.
DuPaul GJ, Power TJ, Anastopoulos A, & Reid R. ADHD rating scale–IV: Checklists, norms, and clinical interpretation. New York, NY: Guilford Press; 1998.
- 30. Taylor SJC, Whincup PH, Hindmarsh PC, Lampe F, Odoki K, & Cook DG. Performance of a new pubertal self-assessment questionnaire: A preliminary study. Paediatr Perinat Epidemiol. 2001;15: 88–94. pmid:11237120
- 31. Birnbaum AS, Lytle LA, Murray DM, Story M, Perry CL, & Boutelle KN. Survey development for assessing correlates of young adolescents’ eating. Am J Health Behav. 2002;26: 284–295. pmid:12081361
- 32. Oldfield RC. The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia. 1971;9: 97–113. pmid:5146491
- 33.
American College of Sports Medicine. ACSM’s guidelines for exercise testing and prescription. 7th ed. New York, NY: Lippincott Williams & Wilkins; 2006.
- 34. Utter AC, Robertson RJ, Nieman DC, & Kang J. Children’s OMNI scale of perceived exertion: Walking/running evaluation. Med Sci Sports Exerc. 2002;34: 139–144. pmid:11782659
- 35.
Freedson PS, & Goodman TL. Measurement of oxygen consumption. In: Rowland TW, editor. Pediatric laboratory exercise testing: Clinical guidelines. Champaign, IL: Human Kinetics; 1993. pp. 91–113.
- 36.
Bar-Or O. Pediatric Sports Medicine for the Practitioner: From Physiologic Principles to Clinical Applications. Springer-Verlag, New York; 1983.
- 37. Shvartz E, & Reibold RC. Aerobic fitness norms for males and females aged 6 to 75 years: a review. Aviat Space Environ Med. 1990;61: 3–11. pmid:2405832
- 38. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;3: 968–980.
- 39. S’egonne F, Salat DH, Dale AM, Destrieux C, Fischl B, van der Kouwe A, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14: 11–22. pmid:14654453
- 40. Gronenschild EHBM, Habets P, Jacobs HIL, Mengelers R, Rozendaal N, van Os J, et al. The effects of Freesurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS ONE. 2012;7: e38234. pmid:22675527
- 41. Dale AM, Fischl B, & Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9: 179–194. pmid:9931268
- 42. Dale AM., & Sereno MI. Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach. Journal of Cognitive Neuroscience. 1993;5: 162–176. pmid:23972151
- 43. Klein A, & Tourville J. 101 labeled brain images and a consistent human cortical labeling protocol. Front Brain Imaging Methods. 2012;6: 171.
- 44.
Wilkinson GS. Wide range achievement test 3 administration manual. Wilmington, Delaware: Jastak Associates; 1993.
- 45. Bull R, & Scerif G. Executive functioning as a predictor of children’s mathematics ability: inhibition, switching, and working memory. Dev Neuropsychol. 2001;19: 273–293. pmid:11758669
- 46. Gernsbacher MA. Less skilled readers have less efficient suppression mechanisms. Psychol Sci. 1993;4: 294–297. pmid:25309046
- 47. St. Clair-Thompson HL, & Gathercole SE. Executive functions and achievements in school: shifting, updating, inhibition, and working memory. Q J Exp Physiol. 2006;59: 745–759.
- 48. Passolunghi MC, & Siegel LS. Short-term memory, working memory, and inhibitory control in children with difficulties in arithmetic problem solving. J Pers Soc Psychol. 2001;80: 44–57.
- 49. Davis CL, Tomporowski PD, McDowell JE, Austin BP, Miller PH, Yanasak NE, Allison JD, & Naglieri JA. Exercise improves executive function and achievement and alters brain activation in overweight children: a randomized, controlled trial. Health Psychol. 2011;30:91–98. pmid:21299297
- 50.
Institute of Medicine. Educating the student body: Taking physical activity and physical education to school. Washington, DC: The National Academies Press; 2013.
- 51. Ng SW, & Popkin BM. Monitoring food and nutrients sold and consumed in the United States: Dynamics and challenges. J Acad Nutr Diet. 2012;112: 41–45. pmid:22389873
- 52. Vaynman S, & Gomez-Pinilla F. Revenge of the “Sit”: How lifestyle impacts neuronal and cognitive health through molecular systems that interface energy metabolism with neuronal plasticity. J Neurosci Res. 2006;84: 699–715. pmid:16862541