Early Human Development 90 (2014) 639–644

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Early Human Development

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Neurodevelopmental pathways to preterm children's specific andgeneral mathematic abilities

Julia Jaekel a,b, Peter Bartmann c, Wolfgang Schneider d, Dieter Wolke b,e,⁎a Department of Developmental Psychology, Ruhr-University Bochum, Bochum, Germanyb Department of Psychology, University of Warwick, Coventry, UKc Institute of Neonatology, University Hospital Bonn, Bonn, Germanyd Department of Psychology, University of Würzburg, Würzburg, Germanye Warwick Medical School, University of Warwick, Coventry, UK

⁎ Corresponding author at: Department of Psychologyand Wellbeing, Warwick Medical School, University of WTel.: +44 24 7657 3217.

E-mail address: D.Wolke@warwick.ac.uk (D. Wolke).

http://dx.doi.org/10.1016/j.earlhumdev.2014.07.0150378-3782/© 2014 Published by Elsevier Ireland Ltd.

a b s t r a c t

a r t i c l e i n f o

Article history:

Received 20 February 2014Received in revised form 15 July 2014Accepted 29 July 2014Available online xxxx

Keywords:Gestational agePretermMathematic abilitiesNeonatal riskVentilation

Background: Preterm children have problems with mathematics but knowledge about the predictors of specificmathematic abilities in preterm populations is scarce.Aims: This study investigated neurodevelopmental pathways to children's general and specific mathematicabilities across the full gestational age range.Study design: Prospective geographically defined longitudinal investigation in Germany.Subjects: 947 children across the full gestational age range (23–41 weeks).Outcome measures. At 8 years, children's cognitive and mathematic abilities were measured and residuals of aregression predicting mathematic scores by IQ were used to identify specific mathematic abilities.Results: Neurodevelopmental cascade models revealed that adverse effects of preterm birth on mathematicabilities were mediated by neonatal risk. Specific mathematic abilities were uniquely predicted by the durationof hospitalization and ventilation.

Conclusions: Prolonged neonatal medical treatment and, in particular, mechanical ventilation may lead to specificimpairments inmathematic tasks. Thesefindings have implications for themodeof respiratory support in neonates,routine follow-up and intervention planning as well as research about brain reorganization after preterm birth.

© 2014 Published by Elsevier Ireland Ltd.

1. Introduction

Mathematic abilities are crucial for lifelong academic attainment aswell as social and occupational functioning [1,2]. Impairments in mathe-matic abilities are common in very preterm (VP) children [3] and partlyaccount for learning disabilities in this population [4]. Thismay be due tothe fact that mathematic performance requires simultaneous processingof complex information which is particularly compromised in pretermchildren [3].

Prematurity is prospectively related to general cognitive abilitiesacross thewhole spectrum of gestational age (GA) and this relationshipis curvilinear with increasingly adverse effects of prematurity withlower GA [5]. Similarly, GA is also related to mathematic abilities, butit is not well established if this relationship is linear [6] or curvilinear[5]. Moreover, longitudinal studies suggest that impairments in mathe-matic skills in very preterm children may be specific and not explainedby global deficits in cognitive function [4] - but it is not known if these

and Division of Mental Healtharwick, Coventry CV4 7AL, UK.

specific mathematic abilities that are independent of general cognitivefunction are related to prematurity across the whole GA range.

In general, knowledge about the nature and underlying neuro-developmental mechanisms of specific mathematic abilities in pretermpopulations is scarce [7]. Prematurity is associated with wide-spreadbrain alterations [8–10]. It has been suggested that the duration ofmechanical ventilation and neonatal treatment may uniquely predictpreterm children'smathematic development [11]. However, gestationalage, birth weight, neonatal complications, and abnormalities in brainstructure are negatively associatedwith both cognitive andmathematicabilities [3]. Thus in order to understand the nature of specific mathe-matic abilities across the total spectrum of GA, the neurodevelopmentalmechanisms explaining general compared with specific mathematicabilities need to be concurrently investigated.

Several studies in normal populations have documented that gener-al cognitive abilities (i.e. central executive of working memory) and at-tention are associated with mathematic abilities in primary schoolchildren [2,12]. Thus early cognitive and attention abilities maymediatethe effects of preterm birth on mathematic abilities. The aim of thisstudy was to determine the pathways to preterm children's specificmathematic abilities compared with the pathways to general mathe-matic abilities.We compared twoneurodevelopmental cascademodels:

640 J. Jaekel et al. / Early Human Development 90 (2014) 639–644

one predicting specific, the other predicting general mathematic abili-ties at 8 years of age in a large sample of children across the whole GAspectrum since birth.

2. Methods

2.1. Procedure

Participating parents were approached within 48 h of the infant'shospital admission and were included in the study once they hadgiven written consent for their child to participate. At 6 and 8 years ofage, children were assessed by an interdisciplinary study team for onewhole day including neurological (done by paediatricians) and cogni-tive assessments (done by psychological assistants). All assessors wereblind to group membership. Ethical permission for the study wasgranted by the Ethics committee of the University of Munich Children'sHospital and the Bavarian Health Council (Landesärztekammer).

2.2. Participants

Data were collected as part of the prospective Bavarian LongitudinalStudy (BLS) [13]. The BLS is a whole-population sample of children bornbetween January 1985 andMarch 1986within a geographically definedarea of Southern Bavaria (Germany) who required admission to achildren's hospital within the first 10 days of life (N = 7505; 10.6% ofall live births). Additionally, 916 healthy term born control infants(normal postnatal medical care) were identified at birth from thesame hospitals in Bavaria during the same period.

Of the initial sample, n=226 survivors born b32 weeks of gestation(n= 26 children diagnosed with severe neurological impairment wereexcluded as they could not participate in the tests), n=248 healthy fullterm control infants, and a subsample of n=473 children born between32 and 38 weeks of gestation (randomly drawnwithin the stratificationfactors gender, socio-economic status and degree of neonatal risk)wereassessed at 20 months as well as at 6 and 8 years of age. Full details ofthe sampling criteria and dropout rates are provided elsewhere [13].Table 1 shows the characteristics of the final sample according to GAgroups (N = 947).

2.3. Measures

2.3.1. Neonatal variablesGestational age (GA) was determined from maternal reports of the

last menstrual period and serial ultrasounds during pregnancy. When

Table 1Sample characteristics of the BLS Phase II study participants.

b32 w GA 32–33

n = 226 n = 87

GA 29.6 (1.5) 32.5 (0Birth weight 1313 (345) 1663 (SGA 28.8% 40.2%Neonatal OPTI scorea 9.44 (2.67) 7.86 (2Duration of initial hospitalization (days) 79 (36) 53 (22Duration of ventilation (days) 17 (20) 5 (9)Child sex (male) 56.6% 47.1%Family SES (1 = low, 6 = high) 3.45 (1.46) 3.49 (1Griffiths scales at 20 months 97 (15) 103 (9Attention regulation at 6 years 5.24 (1.67) 5.76 (1Mathematic Test score at 8 years 92 (16) 96 (17K-ABC MPC score at 8 years 93 (14) 94 (12Specific Maths (residual score)b −0.23 (0.83) −0.13Correlation of Mathematic Test score withK-ABC MPC score at 8 years (r)

.72** .70**

a Higher OPTI scores indicate less optimal neonatal course. Data is presented asmean (SD) fb Specific Maths residual scores were calculated with a regression predicting Mathematic Te

children's scores).

the estimates of these two differed by more than two weeks, postnatalDubowitz scores were used [14]. Birth weight was documented in thebirth records. Infant postnatal complicationswere assessed with a com-prehensive optimality index (OPTI) including 21 items (e.g. intubation,severe anaemia, cerebral haemorrhage) [15]. Duration of ventilationand initial hospitalization length were documented in specific standarddaily medical records.

2.3.2. Family socio-economic background (SES)Information was collected in structured parental interviews within

10days of child birth. Family SESwas computed as aweighted compositescore of the occupation of the self-identified head of each family togetherand the highest educational qualification held by either parent andrecorded as six categories (1 = very low, 6 = very high SES) [16].

2.3.3. Cognitive assessment at 20 months of corrected ageChildren were administered the Griffiths Mental Development

Scales [17] to assess early cognitive development.

2.3.4. Assessment of attention regulation at 6 yearsParticipating children and their mothers were assessed by an inter-

disciplinary study team for one whole day including neurologicalassessments (done by paediatricians) and behaviour ratings (done bypsychologists). All assessors and raters were blind to children's back-ground variables. Child attention regulation across the whole assess-ment day was evaluated as a consensus rating by the whole researchteam (psychologist, assistant psychologist, and paediatrician) [18].

2.3.5. Cognitive and mathematic assessment at age 8 yearsAt primary school age, children's cognitive abilities were assessed

with the German version of the Kaufman Assessment Battery forChildren, K-ABC [19]. In the K-ABC, intelligence is measured as a com-posite score (Mental Processing Component; MPC) based on 8 subteststapping general cognitive functioning.

In order to assess numerical representations and reasoning, childrenwere administered a comprehensive Mathematic Test [20,21]. Test taskswere presented to children in book formwith 79 items assessing numer-ical estimations, calculation, reasoning, andmental rotation abilities. Thetwelve estimation tasks measured children's accuracy in estimatingnumbers and comparing distances between numbers. Retrieval of arith-metic facts and procedural competence were measured with 50 calcula-tion tasks (simple addition) whereas application of these two abilitydimensions on real-world problems was assessed with six reasoningtasks. Finally, children's visual–spatial problem solving was tested with

w GA 34–36 w GA 37–38 w GA 39–41 w GA

n = 202 n = 184 n = 248

.5) 35.1 (0.8) 37.5 (0.5) 40.0 (0.7)378) 2225 (553) 2834 (528) 3463 (424)

39.1% 28.8% 9.3%.57) 5.41 (2.83) 3.21 (2.67) 0.33 (0.56)) 28 (19) 15 (16) 7 (4)

1 (4) 0 (2) 0 (0)50.5% 48.4% 49.6%

.61) 3.69 (1.59) 3.63 (1.62) 3.57 (1.52)) 104 (11) 106 (7) 106 (7).59) 6.17 (1.52) 6.15 (1.45) 6.51 (1.46)) 101 (14) 103 (13) 103 (14)) 98 (12) 100 (9) 101 (10)(0.85) 0.13 (0.78) 0.12 (0.84) 0.06 (0.80)

.67** .51** .61**

or interval scaled and percentages for categorical variables if not indicated otherwise.st scores by K-ABCMPC scores (both z-standardized according to healthy full term control

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eleven mental rotation tasks. Item responses were scored for accuracyand subscale scores were then summed into a comprehensive totalscore representing general mathematic abilities.

All assessments were carried out by trained assistant psychologiststhat were blind to children's background characteristics.

2.3.6. Specific mathematic abilitiesIn order to operationalize specific mathematic abilities that were

independent of general IQ we used residuals of a regression analysispredicting Mathematic Test scores by K-ABC MPC scores.

2.4. Statistical analyses

Data were analysed with SPSS 21.0 and AMOS 21. In order to betterunderstand direct and indirect long-term effects of preterm birth onspecific vs. general mathematic abilities at age 8 years we testedtwo neurodevelopmental cascade models using path analysis. All testscores used as outcome variables at age 8 years in path models werez-standardized according to the healthy full term control children'sscores (n = 248).

3. Results

Preliminary results showed deficits in both general and specificmathematic abilities for very and moderately preterm children (seeTable 1). Curve estimation analysis revealed that the best fitting func-tion for this effect of gestational age group on mathematic abilitieswas quadratic (general math: R2 = .09, F = 48.49, p b 0.001; specificmath: R2 = .03, F = 14.05, p b 0.001, respectively).

3.1. Neurodevelopmental cascade models predicting preterm children'smathematic abilities

Path analysis confirmed that the relationship between GA andmath-ematic abilities at 8 years was best explained by a neurodevelopmentalcascade model. The components of the two final models with specific(residual score, Fig. 1) vs. general mathematic abilities (MathematicTest total score, not controlled for general cognitive abilities, Fig. 2)as dependent variables were identical; however, the underlyingneurodevelopmental pathways were different. Both models fit the dataextremely well: χ2(8) = 3.16, p = .924, CFI = 1.00, RMSEA = .00,90% CI = [.00, .01] for specific mathematic abilities and χ2(7) = 3.02,

Fig. 1.Neurodevelopmental cascade model predicting specificmathematic abilities at age 8 yeaexplained by all predictors; specific maths residual scores were calculated with a regression prefull term control children's scores).

p= .883, CFI= 1.00, RMSEA= .00, 90% CI= [.00, .02] for generalmath-ematic abilities, respectively (please see Table 2 for bivariate correlationdetails).

Both models have in common that the relationship between GAgroup and specific mathematic abilities was re-expressed and enteredinto the model using a quadratic function (GA group2). Controlling forfamily SES and child sex, children of lower GA groups were at higherneonatal risk and in turn these children had lower cognitive abilitiesat age 20 months and lower attention regulation abilities at age6 years. Differences in the two models were found in the followingaspects: the effect of GA on specificmathematic abilities was fully medi-ated by neonatal risk and only the duration of hospitalization and ven-tilation directly predicted specific mathematic abilities 9 years later. Incontrast, the effects of the different neonatal risk variables on generalmathematic abilities were partly mediated by children's cognition andattention abilities, indicating a typical developmental cascade.

Figs. 3 and 4 summarize the direct and indirect effects (standardizedmodel coefficients) of the predictors on specific (Fig. 3) and general(Fig. 4) mathematic abilities: Fig. 3 shows that the total effect of GAgroup2 on specific math abilities was as strong as the total effects ofduration of hospitalization and ventilation (GA group2: 0.15 indirecteffects through the neurodevelopmental cascade; hospitalization:− .14 direct effect plus − .01 indirect effects; and ventilation: − .13direct effect plus − .01 indirect effects, respectively). Fig. 4 shows thatcompared with specific math, the total effects of GA group2 as well asof the neonatal, social, and cognitive predictors on generalmath abilitieswere higher (R2 = .37 versus R2 = .06; GA group2: .30 indirecteffects; hospitalization: − .12 direct effect plus − .12 indirect effects;ventilation:− .14 direct effect plus − .14 indirect effects, respectively).

4. Discussion

This study found that preterm children's general and specific math-ematic abilities decreased exponentially with lower gestational age atbirth. The relationship was quadratic with disproportionally lowermaths scores for children born before 34 weeks of GA. The adverse ef-fects of preterm birth on general and specific mathematic abilities atage 8 years were explained by a neurodevelopmental cascade. Howev-er, the pathway predicting specific maths was different from the onepredicting general mathematic ability: The effects of neonatal risk ongeneral maths were partly mediated by children's cognition and atten-tion abilities whereas specific mathematic abilities were uniquely

rs (N= 947). Please note: *p b 0.05, **p b 0.01, ***p b 0.001; R2 = proportion of variancedictingMaths Test scores by K-ABCMPC scores (both z-standardized according to healthy

Fig. 2. Neurodevelopmental cascademodel predicting generalmathematic abilities at age 8 years (N= 947). Please note: *p b 0.05, **p b 0.01, ***p b 0.001; R2 = proportion of varianceexplained by all predictors; general maths was the Maths Test total score, z-standardized according to healthy full term control children's scores.

642 J. Jaekel et al. / Early Human Development 90 (2014) 639–644

predicted by the duration of hospitalization and ventilation thussuggesting early brain injury and development as originating factor ofspecific mathematic ability.

The comprehensiveOPTI score assessing the occurrence and severityof 21 infant neonatal medical complications did not predictmathematicabilities; however, duration of ventilation, in particular, did. While theOPTI score is a general reliable predictor of early behavioural prob-lems [15], the unweighted summation of diverse neonatal problemsinto one index may not be specific enough to identify early predictorsof mathematic development. In contrast, the association between dura-tion of assisted ventilation and later disability is well documented [22].Recently, Clark et al. (2013) found that preterm birth and duration ofventilation predicted reduced cerebral white matter and caudate vol-ume as well as reduced corpus callosum surface area in adolescencewhereas these neurological structure differences were specifically asso-ciated with poor mathematic development [23]. Our findings suggestthat prolonged periods of mechanical ventilation after preterm birthmay initiate a pathophysiological neurodevelopmental cascade associ-ated with specific mathematic deficiencies. Gestational age per se onlyaccounted for 3% of the variation in specific mathematic abilities at8 years; however, our models showed that the prediction was substan-tially increased by considering effects of prolonged medical treatmentand, in particular, ventilation.

4.1. Strengths and limitations

The data were collected as part of a prospective geographically de-fined whole-population study of children (born 1985/86) across thetotal spectrum of gestational age. Assessments were carried out bytrained assistant psychologists thatwere blind to children's background

Table 2Pearson correlations between the measures used in the neurodevelopmental cascade models (

1 2 3

GA group –

Family SES at birth .03 –

Child sex .05 − .01 –

OPTI − .82* − .03 − .04Duration of hospitalization − .77** − .09* .00Duration of ventilation − .50** .00 − .02Cognition at 20 months .32** .09** .06**Attention regulation at 6 y .28** .17** .13**General maths abilities at 8 y .29** .24** − .04Specific maths abilities at 8 y .14** .08* − .02

Please note: GA groups were coded from 1 = very preterm to 5 = full term; family SES was**p b .01; standardized z-scores were used as indicators of general and specific maths abilities

characteristics. Detailed and comprehensive information on children'smedical treatment and neonatal complications was available; however,sophisticated neuroimaging like MRI was not. Neonatal intensive carehas changed since then (i.e. introduction of corticosteroid and surfac-tant therapies), and has resulted above all in increased survival ofeven lower gestational age infants. Although rates of cognitive problemshave remained at similar levels comparison and cross-validation of ourfindings inmore contemporary cohorts are needed. In the current study,duration of ventilation only represents a proxy measure for possiblehypoxia and perinatal brain injury. In order to fully understand themechanisms explaining the association between duration of ventilationand later mathematic abilities prospective studies assessing neonatalbrain injury with neuroimaging methods are needed.

Path analyses showed that the variance explained in specificmaths (6%) was low compared with the variance explained in generalmathematic abilities (37%) by the same model. While this differenceis explained by our operationalization of specific maths (i.e. theresidualization of mathematic from general cognitive abilities), thevalidity of the neurodevelopmental cascade model was confirmed byexcellent statistical fit values.

In addition to low GA, being born small for gestational age (SGA)carries an increased risk for general cognitive and attention prob-lems [24,25]. Potential effects of SGA on specific mathematic abilitieswere not investigated before although prenatal factors may contributeto the underlying neuropathological mechanisms. In order to explorethe association of SGA birth with neonatal risk and math abilities, wetested the inclusion of SGA as an additional predictor in our pathmodels. No significant paths from SGA to neonatal risk and later devel-opment were found and inclusion significantly deteriorated overallmodel fit. One explanation may be the high multicollinearity between

N = 947).

4 5 6 7 8 9

–

.79** –

.59** .67** –

− .35** − .48** − .50** –

− .30** − .34** − .34** .39** –

− .30** − .40** − .40** .45** .47** –

− .14** − .20** − .21** .16** .15** .73**

coded from 1 = low to 6 = high; child sex was coded 1 = male, 2 = female; *p b .05;at age 8 years.

Fig. 3. Individual predictor effects on specific maths abilities at 8 years. Please note: Totaleffects are direct plus indirect effects; effect estimates are standardizedmodel coefficients(Fig. 1).

643J. Jaekel et al. / Early Human Development 90 (2014) 639–644

SGA and preterm birth. As a result, SGA status and other variables suchas infant birth weight that may be associated with mathematic abilitieswere excluded from our models.

4.2. Theoretical and practical implications

Our findings on specific mathematic abilities have a number of im-plications for clinical practise and intervention planning as well as re-search on brain reorganization after preterm birth. Preterm childrenwith specific mathematic impairments that are independent of generalIQ are a special group characterized by severe neonatal risk. Ourneurodevelopmental cascade model suggests that with increasing pre-maturity and length of neonatal treatment (i.e. mechanical ventilationin particular) children are at an increased risk of specific mathematicdeficits. Thus increased screening and intervention efforts during thepre- and primary school years may be recommended for those childrenat highest risk in order to help themobtainmathematic learning goals inclass and prevent academic failure. For example, mathematic lessonsin school could be tailored to preterm children's specific needs who re-quire a more adaptive and possibly slower learning approach (e.g. byexplicit, direct math instruction [2]) while parents could actively facili-tate attention regulation and school success with sensitive instruction[26,27].

The cognitive processes underlying mathematic abilities criticallyrely on fine-tuned activations of distributed fronto-parieto-temporalbrain areas [28] and intact brain connectivity across these areas is essen-tial. Preterm birth is associated with aberrant brain connectivity [10]

Fig. 4. Individual predictor effects on generalmaths abilities at 8 years. Please note: Totaleffects are direct plus indirect effects; effect estimates are standardizedmodel coefficients(Fig. 2).

and white matter disruption [23], resulting in abnormal activationpatterns. It will be important to investigate if differences in pretermchildren's brain structure and connectivity are associated with specificmathematic deficiencies.

5. Conclusion

Preterm children's specific mathematic abilities decrease exponen-tially with lower GA. The duration of neonatal medical treatment and,in particular, mechanical ventilation may lead to brain alterations andsubsequent specific impairments in mathematic tasks. These findingsmay have implications for themode of respiratory support in neonates,routine follow-up and intervention planning as well as research aboutbrain reorganization after preterm birth.

Conflict of interest statement

There are no conflicts of interest.

Acknowledgements

Wewould like to thankDr. Samantha Johnson for her feedback on anearlier draft of this article. This study was supported by grant JA 1913from the German Research Foundation (DFG) and by grants PKE24,JUG14, 01EP9504 and 01ER0801 from the German Federal Ministry ofEducation and Science (BMBF). The contents are solely the responsibil-ity of the authors and do not represent the official views of the DFG orthe BMBF.

References

[1] Geary DC. Consequences, characteristics, and causes of mathematical learningdisabilities and persistent low achievement in mathematics. J Dev Behav Pediatr2011;32(3):250–63.

[2] Geary DC. Early foundations for mathematics learning and their relations to learningdisabilities. Curr Dir Psychol Sci 2013;22:23–7.

[3] Taylor HG, Espy KA, Anderson PJ. Mathematics deficiencies in childrenwith very lowbirth weight or very preterm birth. Dev Disabil Res Rev 2009;15(1):52–9.

[4] SimmsV, Gilmore CK, Cragg L,MarlowN,Wolke D, Johnson S.Mathematics difficultiesin extremely preterm children: evidence of a specific deficit in basic mathematicsprocessing. Pediatr Res 2013;73(2):236–44.

[5] Jaekel J, Baumann N, Wolke D. Effects of gestational age at birth on cognitive perfor-mance: a function of cognitive workload demands. PLoS One 2013;8(5):e65219.

[6] Lipkind HS, Slopen ME, Pfeiffer MR, McVeigh KH. School-age outcomes of latepreterm infants in New York City. Am J Obstet Gynecol 2012;206(3):222.e1–6.

[7] Simms V, Cragg L, Gilmore C, Marlow N, Johnson S. Mathematics difficulties inchildren born very preterm: current research and future directions. Arch Dis ChildFetal Neonatal Ed 2013;98(5):F457–63.

[8] Dudink J, Kerr JL, Paterson K, Counsell SJ. Connecting the developing preterm brain.Early Hum Dev 2008;84(12):777–82.

[9] Kapellou O, Counsell SJ, Kennea N, Dyet L, Saeed N, Stark J, et al. Abnormal corticaldevelopment after premature birth shown by altered allometric scaling of braingrowth. PLoS Med 2006;3(8):e265.

[10] Bäuml JG, Daamen M, Meng C, Neitzel J, Scheef L, Jaekel J, et al. Correspondence be-tween aberrant intrinsic network connectivity and gray matter volume in the ven-tral brain of preterm born adults. Cereb Cortex 2014. http://dx.doi.org/10.1093/cercor/bhu133.

[11] Espy KA, Fang H, Charak D, Minich N, Taylor HG. Growth mixture modeling of aca-demic achievement in children of varying birth weight risk. Neuropsychology2009;23(4):460–74.

[12] Bull R, Espy KA, Wiebe SA. Short-term memory, working memory, and executivefunctioning in preschoolers: longitudinal predictors of mathematical achievementat age 7 years. Dev Neuropsychol 2008;33(3):205–28.

[13] Wolke D, Meyer R. Cognitive status, language attainment, and prereading skillsof 6-year-old very preterm children and their peers: the Bavarian LongitudinalStudy. Dev Med Child Neurol 1999;41:94–109.

[14] Dubowitz LM, Dubowitz V, Goldberg D. Clinical assessment of gestational age in thenewborn infant. J Pediatr 1970;77(1):1–10.

[15] Schmid G, Schreier A, Meyer R, Wolke D. Predictors of crying, feeding and sleepingproblems: a prospective study. Child Care Health Dev 2011;37(4):493–502.

[16] Bauer A. Ein Verfahren zur Messung des fuer das Bildungsverhalten relevantenSozial Status (BRSS)—ueberarbeitete Fassung. Frankfurt: Deutsches Institut fürInternationale Pädagogische Forschung; 1988.

[17] Brandt I. Griffiths Entwicklungsskalen (GES zur Beurteilung der Entwicklung in denersten beiden Lebensjahren). Weinheim: Beltz; 1983.

644 J. Jaekel et al. / Early Human Development 90 (2014) 639–644

[18] Jaekel J, Wolke D, Bartmann P. Poor attention rather than hyperactivity/impulsivitypredicts academic achievement in very preterm and fullterm adolescents. PsycholMed 2013;43:183–96.

[19] Melchers P, Preuss U. K-ABC: Kaufman Battery for Children: DeutschsprachigeFassung. Frankfurt, AM: Swets & Zeitlinger; 1991.

[20] Wolke D, Leon-Villagra J. Mathematiktest für Grundschulkinder. Munich: BavarianLongitudinal Study; 1993.

[21] Jaekel J, Wolke D. Preterm birth and dyscalculia. J Pediatr 2014;164(6):1327–32.[22] Grégoire MC, Lefebvre F, Glorieux J. Health and developmental outcomes at

18 months of very preterm infants with bronchopulmonary dysplasia. Pediatrics1998;101:856–60.

[23] Clark CA, Fang H, Espy KA, Filipek PA, Juranek J, Bangert B, et al. Relation of neuralstructure to persistently low academic achievement: a longitudinal study of childrenwith differing birth weights. Neuropsychology 2013;27(3):364–77.

[24] Gutbrod B, Wolke D, Söhne B, Ohrt B, Riegel K. The effects of gestation andbirthweight on the growth and development of very low birthweight small forgestational age infants: a matched group comparison. Arch Dis Child 2000;82(3):F208–14.

[25] Hall J, Jaekel J, Wolke D. Gender distinctive impacts of prematurity and small forgestational age on age 6 attention problems. Child Adolesc Mental Health 2012;17(4):238–45.

[26] Wolke D, Jaekel J, Hall J, Baumann N. Effects of sensitive parenting on the academicresilience of very preterm and very low birth weight adolescents. J Adolesc Health2013;53(5):642–7.

[27] Jaekel J, Wolke D, Chernova J. Mother and child behaviour in very preterm andfullterm dyads at 6.3 and 8.5 years. Dev Med Child Neurol 2012;54:716–23.

[28] Menon V. Developmental cognitive neuroscience of arithmetic: implications forlearning and education. ZDM 2010;42(6):515–25.