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Neuropsychologia 42 (2004) 1118–1131
Head size and intelligence, learning, nutritional status
and brain development
Head, IQ, learning, nutrition and brain
Daniza M. Ivanovica,∗, Boris P. Leivaa, Hernán T. Péreza, Manuel G. Olivaresa, Nora S. D´
ıaza,
Mar´
ıa Soledad C. Urrutiab, Atilio F. Almagiàc, Triana D. Toroc, Patricio T. Millerd,
Enrique O. Boschd, Cristián G. Larra´
ınd
aPublic Nutrition Area, Institute of Nutrition and Food Technology (INTA), University of Chile, Avda. Macul 5540, P.O. Box 138-11, Santiago, Chile
bPan American Health Organization (PAHO), Pan American Sanitary Bureau, Regional Office of the World Health Organization, Washington, DC, USA
cLaboratory of Physical Anthropology and Human Anatomy, Institute of Biology, Catholic University of Valpara´ıso, Avda. Brasil 2959, Valpara´ıso, Chile
dDepartment of Magnetic Resonance Imaging Service, German Clinic of Santiago, Avda. Vitacura 5951, Santiago, Chile
Received 22 September 2003; accepted 17 November 2003
Abstract
This multifactorial study investigates the interrelationships between head circumference (HC) and intellectual quotient (IQ), learning,
nutritional status and brain development in Chilean school-age children graduating from high school, of both sexes and with high and low
IQ and socio-economic strata (SES). The sample consisted of 96 right-handed healthy students (mean age 18.0±0.9 years) born at term.
HC was measured both in the children and their parents and was expressed as Z-score (Z-HC). In children, IQ was determined by means
of the Wechsler Intelligence Scale for Adults-Revised (WAIS-R), scholastic achievement (SA) through the standard Spanish language and
mathematics tests and the academic aptitude test (AAT) score, nutritional status was assessed through anthropometric indicators, brain
development was determined by magnetic resonance imaging (MRI) and SES applying the Graffar modified method. Results showed that
microcephalic children (Z-HC ≤2S.D.) had significantly lower values mainly for brain volume (BV), parental Z-HC, IQ, SA, AAT, birth
length (BL) and a significantly higher incidence of undernutrition in the first year of life compared with their macrocephalic peers (Z-HC >
2 S.D.). Multiple regression analysis revealed that BV, parental Z-HC and BL were the independent variables with the greatest explanatory
power for child’s Z-HC variance (r2=0.727). These findings confirm the hypothesis formulated in this study: (1) independently of age,
sex and SES, brain parameters, parental HC and prenatal nutritional indicators are the most important independent variables that determine
HC and (2) microcephalic children present multiple disorders not only related to BV but also to IQ, SA and nutritional background.
© 2004 Elsevier Ltd. All rights reserved.
Keywords: Head; Brain; Intelligence; Nutrition assessment; Magnetic resonance imaging; Education
1. Introduction
Our previous studies reveal that head circumference,
the anthropometric index of both nutritional background
and brain development, is the most relevant physical in-
dex associated with scholastic achievement and intellectual
ability in Chilean school-age children (Ivanovic, Forno,
Castro, & Ivanovic, 2000b;Ivanovic, Ivanovic, Truffello,
& Buitrón, 1989;Ivanovic, Olivares, Castro, & Ivanovic,
1996;Toro, Almagià, & Ivanovic, 1998). A “normal” head
∗Corresponding author. Tel.: +56-2-678-1459; fax: +56-2-221-4030.
E-mail addresses: inta8@abello.dic.uchile.cl, daniza@uec.inta.uchile.cl
(D.M. Ivanovic).
URL: http://www.inta.cl.
circumference, mean ±2 standard deviations, could be
more related to statistical normality, although this may
not be the case for psychological function or educational
achievement (Ivanovic et al., 2000b). Despite the fact that
microcephaly and macrocephaly are considered reliable
indicators of brain pathology, head circumference values
below the mean are associated with an increased incidence
of lower intellectual abilities. This means that small differ-
ences in head size could be important in the interrelationship
head circumference–intelligence–learning (Ivanovic et al.,
2000b; Menkes, 1995).
The explanatory power of head circumference on in-
tellectual ability and scholastic achievement variances in-
creases significantly from the onset of elementary school
until the end of high school, in contrast, the explanatory
0028-3932/$ – see front matter © 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.neuropsychologia.2003.11.022
D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131 1119
power of body weight and body height decrease signifi-
cantly (Ivanovic et al., 1996, 2000b). Even more, school
dropout correlates with head circumference and not with
weight or height; at the onset of elementary school 59% of
children had suboptimal head circumferences, percentage
that decreased significantly to 40% in high school graduates
(Ivanovic et al., 1996).
The relationships between head circumference, brain de-
velopment and intelligence have been studied since the time
of Broca and Galton, who concluded that variations in brain
size (estimated indirectly by measuring head circumference)
are related with intelligence (Vernon, Wickett, Bazana, &
Stelmack, 2000). Throughout the XX century, many investi-
gators tried to establish the biological basis of human intelli-
gence. The findings of several of these studies demonstrated
a positive and significant correlation between head circum-
ference, brain size and intelligence (Botting, Powls, Cooke,
& Marlow, 1998;Desch, Anderson, & Snow, 1990;Dolk,
1991;Ivanovic et al., 2000a,b,d;Nelson & Deutschberger,
1970;Ounsted, Moar, & Scott, 1988;Reiss, Abrams, Singer,
Ross, & Denckla, 1996;Rushton, 2000;Strauss & Dietz,
1998;Vernon et al., 2000;Willerman, Schultz, Rutledge, &
Bigler, 1991). Even in the elderly, head circumference has
been found to positively and significantly correlated with
intelligence. In this context, some authors have emphasised
that bigger heads or brains may protect people against
intellectual impairment (Schofield, Logroscino, Andrews,
Albert, & Stern, 1997;Tisserand, Bosma, Van Boxtel,
& Jolles, 2001).
Some authors have reported a non-significant association
between brain size and intelligence. Many of these studies
provided controversial evidence about the relationship be-
tween brain size and intelligence and some of them, carried
out in monozygotic twins or sisters, did not find any as-
sociation between these variables (Schoenemann, Budinger,
Sarich, & Wang, 2000;Teasdale & Pakkenberg, 1988;Yeo,
Turkheimer, Raz, & Bigler, 1987). Nevertheless, recent find-
ings from other investigators, also in monozygotic and dizy-
gotic twins, found a positive correlation between brain size
and intelligence (Anderson, 1999; Pennington et al., 2000).
Therefore, genetic and environmental factors could affect
brain development, intelligence and head circumference, be-
sides this, could determine prenatal and postnatal nutritional
status and educational attainment (Baker, Treloar, Reynolds,
Heath, & Martin, 1996;Casto, DeFries, & Fulker, 1995;
Luke, Keith, & Keith, 1997;McGue & Bouchard, 1998;
Strauss & Dietz, 1998;Weaver & Christian, 1980).
Several communications have described that head cir-
cumference in the first year of life may predict later intelli-
gence (Botting et al., 1998;Nelson & Deutschberger, 1970).
In this respect, the interrelationship between intelligence
and nutritional background, reflected by a decreased head
circumference, can be affected by birth weight and other
variables (Botting et al., 1998; Ivanovic, 1996;Ivanovic
et al., 1989, 1996, 2000a,d, 2002;Leiva et al., 2001;Matte,
Bresnahan, Begg, & Susser, 2001;Pennington et al., 2000;
Reiss et al., 1996; Rushton, 2000; Sorensen et al., 1999;
Stathis, O’Callaghan, Harvey, & Rogers, 1999;Stoch,
Smythe, Moodie, & Bradshaw, 1982;Toro et al., 1998;
Vernon et al., 2000; Willerman et al., 1991). However, other
authors found that impaired fetal growth was not associated
with poorer cognitive performance in adult life; adaptations
made by the fetus in response to conditions that retard
growth seem to be largely successful in maintaining brain
development (Martyn, Gale, Sayer, & Fall, 1996).
Intelligence has been described as the best predictor of
school achievement (Ivanovic et al., 1989, 2000a,c,d, 2002)
and significantly explained by maternal intellectual quotient
(IQ), by brain volume and nutritional status during the first
year of life, as we reported previously in the sample eval-
uated for the present study (Ivanovic et al., 2002); this has
been observed independently of age, sex and socio-economic
stratum (SES). Results from other studies carried out by us
in Chilean school-age children to determine the interrela-
tionship between intellectual ability and socio-economic,
cultural, family, mass media exposure, demographic and
educational factors showed maternal schooling was the
variable with the greatest explanatory power in intellectual
ability variance (Ivanovic, Forno, & Ivanovic, 2001).
The impact of early childhood malnutrition on head cir-
cumference, brain development and later on intelligence is
still a matter of controversy due to the fact that these vari-
ables are influenced by socio-economic and cultural factors
that are co-determinants of intelligence, of nutritional status
and of brain development; head circumference below −2
S.D. of the mean may be an indicator of severe undernutri-
tion and accurately reflects retarded brain growth during the
first year of life (Winick & Rosso, 1969a). The long-term
effects of severe undernutrition at an early age may result in
delayed head circumference growth, delay of brain develop-
ment and decreased intelligence and scholastic achievement,
variables that are strongly interrelated (Grantham-McGregor
& Fernald, 1997;Ivanovic, 1996; Ivanovic et al., 2000d,
2002; Leiva et al., 2001; Stoch et al., 1982;Winick &
Rosso, 1969a). Malnutrition alters brain development and
intelligence in a multicausal context, and poverty and de-
privation exacerbate these negative effects, especially when
these factors persist throughout the lifetime of the indi-
vidual. Under these conditions, a stable environment that
provides adequate stimulation is very difficult to achieve
and for this reason the brain damage caused by malnutrition
at an early age frequently is not reversible (Brown & Pollitt,
1996).
In a multifactorial approach, the aim of this study was
to determine the interrelationships between the head size
and intelligence, learning, nutritional status, brain develop-
ment and parental head size in healthy Chilean school-age
children graduating from high school, of both sexes, with
high and low intellectual quotient and of the high and
low socio-economic strata. The final purpose was to con-
firm the hypothesis that: (1) independently of age, sex
and socio-economic strata, brain parameters, parental HC
1120 D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131
and prenatal nutritional indicators are the most important
independent variables that determine HC and (2) micro-
cephalic children present multiple disorders not only re-
lated to BV but also to IQ, SA and nutritional background,
variables that are significantly interrelated.
2. Methods
2.1. Participants
The final sample consisted of 96 right-handed, healthy
high school graduate students born at term (mean age 18.0±
0.9 years) who had no history of alcoholism, or symptoms of
brain damage, intrapartum fetal asphyxia, hyperbilirubine-
mia, epilepsy, or heart disease and whose mothers had no
history of smoking, alcoholism or drug intake before and
during pregnancy. The sample was chosen from among 1817
school-age children, the total population graduating from
high school who attended public and private schools in the
richest and the poorest counties of the Chile’s Metropolitan
Region, according to the UNICEF classification (UNICEF,
1994). Intellectual quotient (Wechsler Intelligence Scale for
Adults-Revised (WAIS-R)), socio-economic stratum and sex
were considered for sample selection. A comparative study
of two groups of Chilean high school graduates was car-
ried out: group 1, high IQ (≥120 WAIS-R) and group 2,
low IQ (<100 WAIS-R). The total IQ of the school-age
children from the group 1 (125.4±5.5; n=47) was sig-
nificantly higher than those from the group 2 (91.4±6.8;
n=49) (t=26.934 P<0.0001) and this was observed for
both verbal and non-verbal IQ, as we reported previously
(Ivanovic et al., 2002). IQ (total, verbal and non-verbal) did
not differ by sex in both IQ levels and SES. The same pro-
portion of school-age children according to SES (high and
low) (1:1) and sex (1:1) were included in each IQ group
(Ivanovic et al., 2002). This study was approved by the
Committee on Ethics in Studies in Humans of the Institute
of Nutrition and Food Technology (INTA), University of
Chile. The subjects’ consent was obtained according to the
Declaration of Helsinki (The World Medical Association,
1964).
2.2. SES
SES was assessed applying the Graffar modified scale
adapted for Chilean urban population that considers items
such as schooling, job held by the head of the household,
characteristics of the house (building materials, ownership
status, water supply, sewerage and ownership of durable
goods) (Alvarez, Muzzo, & Ivanovic, 1985). The scale clas-
sifies a population into six socio-economic strata: (1) high;
(2) medium-high; (3) medium; (4) medium-low; (5) low and
(6) extreme poverty. In the present study, only high (1 +2)
and low (4+5+6) SES were considered because they rep-
resent extreme SES conditions.
2.3. Nutritional status
Nutritional status was assessed by means of measure-
ments of weight (W), height (H), head circumference (HC),
arm circumference (AC) and triceps skinfold (TS) that
were performed by the principal author using standardised
procedures (Gibson, 1990). Weight-for-age Z-score (Z-W)
and height-for-age Z-score (Z-H) were not calculated since
most part of the sample was older than 18 years and WHO
tables (WHO (1980) could not be applied. The body mass
index, W/H2(BMI) was compared with the Garrow norms
(Garrow, 1981).
HC was compared with the tables of Ivanovic et al.
(1995),Nellhaus (1968),Roche, Mukherjee, Guo and
Moore (1987) and Tanner (1984), by sex and age and was
expressed as Z-score (Z-HC); in this respect, mean±2S.D.
was considered “normal HC”, <−2 S.D. microcephaly and
>2 S.D. macrocephaly. Z-HC values were the same when
applying the different tables because the correlation co-
efficient between these patterns was 0.98 (Ivanovic et al.,
1995). In this study, HC values were compared with the ta-
bles of Roche et al. (1987) and were categorised as follows:
<−2, −2to<0, 0–2 and >2 S.D., considering that, as we
said previously, HC values immediately under the mean are
associated with an increased incidence of lower intellectual
ability (Ivanovic et al., 2000b).
Percentages of adequacy to the median of arm circum-
ference-for-age (% AC/A), triceps skinfold-for-age (%
TS/A), arm muscle area-for-age (% AMA/A) and arm fat
area-for-age (% AFA/A) were calculated using data from
Frisancho (1981). Birth weight (BW) and birth length (BL)
were used as indexes of prenatal nutrition, Z-HC and %
AMA/A, served as indicators of postnatal nutrition and the
BMI was used as an index of current nutritional status. These
data were complemented with those obtained from the births
register at the National Registry Office. Parents were inter-
viewed to obtain information about the child’s previous nu-
tritional diseases, especially undernutrition at an early age.
2.4. Study of brain development
Brain development was evaluated at the German Clinic
of Santiago by magnetic resonance imaging (MRI) accord-
ing to standardised techniques (Willerman et al., 1991).
Using the lowest margin of the cerebellum in a midsagit-
tal view to align the first axial (horizontal) MRI slice, 18
mixed-weighted images (spin-echo pulse sequence with a
TR of 2000ms and a TE of 30 ms) were obtained from a
Signa MRI GE (General Electric Medical Systems, Milwau-
kee, Wisconsin, USA) unit with field strength of 1.5T. All
slices were 5 mm thick and separated by 2.5 mm. Each image
was 256×256 pixels with 256 levels of gray. The MRI tape
was read into a visual analog scale (VAS), computed and the
image analysed after removing identifying information. A
trained specialist without foreknowledge of either IQ or sex
carried out the analyses. For each slice, a Roberts gradient
D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131 1121
traced the boundary of the scalp by outlining large-intensity
differences between adjacent pixels. All gray scale intensity
values of <96 within this boundary were converted to zero.
This deleted the skull, most of the meninges, and the in-
terhemispheric fissure; other brain membranes were deleted
manually with a cursor. The computer then counted all pix-
els with nonzero gray scale values for brain size in each
slice, their summed value serving as the index for overall
brain size. Cortex thickness data, brain volume (BV), bi-
parietal diameter (BD) and anteroposterior diameter (APD),
corpus callosum (CC) length, thickness of genu (CCGT),
body (CCBT) and splenium (CCST), the presence of neu-
ronal migration disorders, qualitative and quantitative evalu-
ation of white matter, cortical and basal subarachnoid space
and ventricular system size are reported.
Some authors emphasise that, at present, there is no
meaningful basis for the comparison of brain sizes within
and between racial groups and sexes; the control for body
size across racial groups (and sexes) is rendered difficult
because bodies do not just differ only in Hand W(Peters
et al., 1998). In the present study, the correlations between
BV and Hand Wwere very low, as we informed in a
previous report (Ivanovic et al., 2002) and the analysis of
covariance (Guilford & Fruchter, 1984) revealed that no
significant effect of sex, Hand Wwas observed for BV;
however, despite of this, values were adjusted by body size
(Wand H) but were so similar to absolute values that only
these are reported in the present study. In the same way, CC
length was adjusted for BV and CCGT, CCBT and CCST
were adjusted for CC length using analysis of covariance
(Frodl et al., 2001;Guilford, & Fruchter, 1984;Matano
& Nakano, 1998), but adjusted values were so similar to
absolute values that only these are reported.
2.5. Intellectual quotient
Obviously, human intelligence exceeds all that is mea-
sured by an IQ test score, but most studies have defined
“intelligence” operationally as performance on IQ or simi-
lar tests. In this study, IQ (total, verbal and non-verbal) was
assessed by means of the Wechsler Intelligence Scale for
Adults-Revised adapted for Chilean population and was car-
ried out at the school (Wechsler, 1981; Hermosilla, 1986).
WAIS-R consists of a set of six verbal and five non-verbal
subtests that are individually administered requiring about
1.5h, and yields an age-corrected estimate of IQ. To avoid
examiner bias, the WAIS-R was administered separately to
each child in quiet rooms by a team of educational psycholo-
gists specially trained in this type of study. Before each phase
of the test, the psychologist provided a clear explanation to
each child, in order to clarify the problem to be solved.
2.6. Scholastic achievement test (SAT)
SA was evaluated through standard Spanish language
(LA) and mathematics (MA) tests especially designed for
this study. Content validity was based on the fact that the test
was designed taking into consideration the objectives pur-
sued by the curricular programmes of the Chilean Ministry
of Education (Chile Ministerio de Educación Pública, 1996).
The items tested were 51 for LA and 65 for MA. A pilot test
was carried out in 160 school-age children during which re-
liability was determined applying the Spearman-Brown cor-
relation, scores being 0.92 and 0.97 for LA and MA, respec-
tively, when comparing paired and unpaired items (Guilford
& Fruchter, 1984). Item-test consistency for each item was
measured by Pearson correlation scoring values above 0.30
in all of them (Guilford & Fruchter, 1984). Results were
expressed as percentage of achievement in overall results
(SAT) (((LA +MA)/116) ×100), as well as LA ((LA/51)
×100) and MA ((LA/65) ×100). Besides these tests, the
academic aptitude test (AAT), the baccalaureate examina-
tion for university admission with national coverage, was
also considered, both verbal (90 items) and mathematics (60
items) tests, with a maximum score of 900 in each test. The
overall results in the AAT were also calculated (verbal +
mathematics)/2.
2.7. Statistical analysis
Data were analysed by means of covariance tests, variance
tests, Scheffe’s test for comparison of means, correlation,
multiple regression and chi-square test (χ2) that was used
to determine significant differences between the categorical
variables using the Statistical Analysis System (SAS) pack-
age (Guilford & Fruchter, 1984;SAS, 1983).
3. Results
3.1. Distribution of the sample of Chilean high school
graduates by Z-HC categories and total IQ group
As regards to Z-HC, 5.2 and 3.1% of the sample reg-
istered values <−2 S.D. (microcephaly) and >2 S.D.
(macrocephaly), respectively. Most of the sample, 91.7%,
had a “normal HC” (mean ±2S.D.); however 57.3% ex-
hibited Z-HC values between −2to<0 S.D. and 34.4%,
between 0 and 2 S.D., without significant differences by
sex.
The distribution of the sample by Z-HC categories and
total IQ group is shown in Fig. 1. Considering that children
were paired by SES in each IQ group, it can be observed
that all students with Z-HC values <−2 and >2 S.D. had
low and high IQ, respectively. Although the category −2to
<0 S.D. falls within the “normal” range for HC, the percent-
age of children with low IQ (60.0%) increases significantly
compared with their peers with high IQ (40%). The oppo-
site is observed in the category 0–2 S.D. (33.3 and 66.7%,
respectively) (P<0.01). This was observed both males and
females.
1122 D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131
Fig. 1. Distribution of the sample of Chilean high school graduates by head
circumference-for-age Z-score (Z-HC) categories and total intellectual
quotient (IQ) group. χ2
o(1)=9.674 > χ2
t(1)0.01 =6.635. Chi-square was
calculated comparing Z-HC values <0 and ≥0 S.D.
3.2. Mean age, IQ, SA and AAT score by Z-HC categories
Table 1 summarises the mean ±S.D.of age, IQ, SA and
AAT score by Z-HC categories. School-age children of both
sexes with Z-HC values <−2 S.D. were significantly older
compared with their peers with values between 0 and 2 S.D.
and >2 S.D. (P<0.01). In fact, the group with Z-HC values
<−2 S.D. were 1.5 years older than those with the great-
est Z-HC values. The mean total IQ in school-age children
with Z-HC values <−2 S.D. was 33 points lower than those
with values >2 S.D. (P<0.01). In verbal and non-verbal
IQ, this difference was 40.9 points (P<0.001) and 17.9
points (P<0.05), respectively and the same was observed
in both sexes. Total, verbal and nonverbal IQ values were
similar within −2to<0 S.D. group and within 0–2 S.D.
Table 1
Age, intellectual quotient (IQ), scholastic achievement (SA) and academic aptitude test (AAT) score of Chilean high school graduates by head
circumference-for-age Z-score (Z-HC) categoriesa
Z-HC F
<−2 S.D. (5) −2to<0 S.D. (55) 0–2 S.D. (33) >2 S.D. (3)
Age (years) 18.9a ±0.3 18.1ab ±1.0 17.7b ±0.5 17.4b ±0.4 4.20∗∗
IQ (score)
Total 91.0a ±7.3 104.3a ±17.9 115.4b ±16.6 124.0b ±5.3 5.50∗∗
Verbal 87.8a ±5.7 104.2ab ±18.5 114.8b ±16.6 128.7b ±2.1 6.30∗∗∗
Non-verbal 96.4a ±9.8 104.2ab ±16.2 113.9b ±15.8 114.3b ±13.7 3.61∗
SA (% of correct responses)
Overall results 8.6a ±9.2 46.6ab ±26.9 56.6b ±26.6 78.8b ±12.1 4.53∗∗
LA 29.0a ±12.9 51.8ab ±22.9 60.5ab ±25.1 81.7b ±11.8 4.26∗∗∗
MA 11.5a ±9.5 43.1ab ±32.3 52.8ab ±32.7 76.8b ±12.9 3.51∗
AAT (score) (2) (48) (31) (3)
Overall results 403.3a ±42.7 545.0b ±158.1 643.7bc ±145.6 744.8c ±39.5 4.79∗∗
Verbal AAT 383.5a ±61.5 521.9a ±153.3 637.6b ±152.0 719.7b ±38.1 5.76∗∗
Mathematics AAT 423.0a ±19.8 568.2ab ±169.8 649.9b ±148.6 770.0b ±42.6 3.62∗
aResults are expressed as mean ±S.D.The number of cases is indicated between parentheses. Means with the same letter are not significantly
different at the 0.05 level based on Scheffe’s test. F, ANOVA; LA, Spanish language achievement; MA, mathematics achievement.
∗P<0.05.
∗∗ P<0.01.
∗∗∗ P<0.001.
group, although values were significantly higher in this lat-
ter group. School-age children with Z-HC values <−2 S.D.
attained significantly lower percentages of correct responses
in the SAT (8.6%), in comparison with their peers whose
Z-HC values were between 0 and 2 S.D. (56.6%) and >2
S.D. (78.8%) (P<0.01). This was also observed in both
sexes for both LA (P<0.001) and MA (P<0.05). Thus,
in both sexes, performance in the SAT was very low in
school-age children with Z-HC values <−2 S.D. of whom
80 and 20% presented SAT scores <25% and between 25
and 49% of correct responses, respectively. None of the sub-
jects in this group scored ≥50% of correct responses. On
the contrary, in the group with Z-HC values >2 S.D., 33.3
and 66.7% achieved between 50 and 74% and ≥75% of
correct responses, respectively and none of them presented
scores <50%. In the total mean AAT, school-age children
with Z-HC values <−2 S.D., both males and females, scored
significantly lower than their peers from the other groups
of the sample (P<0.01). In fact, school-age children with
Z-HC values <−2 and >2 S.D. scored 44.8% versus 82.8%
of achievement in the total mean AAT, respectively. Verbal
and mathematics AAT scores exhibited similar results with
respect to the mean total AAT. All children with Z-HC val-
ues <−2 S.D. registered AAT scores <p25 (<450 points)
and in the >2 S.D. group, 33.3% scored between p50 and
<p75 (631–736 points) and most of them, 66.7%, scored
≥p75 (≥737 points).
3.3. Nutritional parameters by Z-HC categories
Nutritional parameters by Z-HC categories are described
in Table 2. School-age children of both sexes with Z-HC
D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131 1123
Table 2
Nutritional parameters of Chilean high school graduates by head circumference-for-age Z-score (Z-HC) categoriesa
Nutritional parameters Z-HC F
<−2 S.D. (5) −2to<0 S.D. (55) 0–2 S.D. (33) >2 S.D. (3)
Prenatal nutritional background
BW (g) 2750.0 ±316.2 3062.1 ±495.2 3170.3 ±658.2 3716.7 ±332.9 2.25 (t)
BL (cm) 46.0a ±4.5 48.9ab ±2.8 49.6ab ±2.9 51.3b ±1.2 2.82∗
Postnatal nutritional background
Body W(kg)
Males 48.4a ±2.2 (3) 65.3b ±9.4 (24) 66.9b ±4.9 (18) 73.8b ±1.4 (2) 6.16∗∗
Females 47.5 ±3.6 (2) 55.8 ±6.9 (31) 60.2 ±11.3 (15) 63.5 ±0.0 (1) 1.95
Body H(cm)
Males 166.0 ±8.6 (3) 170.7 ±6.2 (24) 171.6 ±6.2 (18) 176.0 ±3.5 (2) 1.15
Females 156.9ab ±7.6 (2) 157.6a ±5.0 (31) 163.3b ±6.8 (15) 160.6ab ±0.0 (1) 3.54*
HC (cm)
Males 51.7a ±0.9 (3) 54.9b ±0.6 (24) 56.8c ±0.7 (18) 59.7d ±0.3 (2) 90.72∗∗∗∗
Females 51.9a ±0.4 (2) 53.9b ±0.6 (31) 55.6c ±0.8 (15) 57.5c ±0.0 (1) 41.20∗∗∗∗
Z-HC
Males −3.17a ±0.64 (3) −0.67b ±0.48 (24) 0.78c ±0.53 (18) 2.98d ±0.21 (2) 91.65∗∗∗∗
Females −2.63a ±0.30 (2) −0.68b ±0.45 (31) 0.57c ±0.54 (18) 2.01c ±0.00 (1) 46.21∗∗∗∗
Current nutritional status
BMI (P/T2) 18.3a ±1.2 22.4b ±2.7 22.7b ±3.4 24.1b ±1.1 3.97∗
Brachial anthropometric parameters
%AC/A79.7a ±10.8 96.4b ±11.3 98.5b ±11.9 101.4b ±4.5 4.14∗∗
% TS/A72.8 ±24.2 111.6 ±42.0 119.3 ±59.3 120.8 ±34.7 1.39
% AMA/A67.2a ±17.0 90.3ab ±16.9 95.3b ±22.7 103.1b ±16.0 3.57∗
%AFA/A60.1 ±24.3 110.2 ±48.7 118.5 ±63.0 122.2 ±28.0 1.80
aResults are expressed as mean ±S.D. The number of cases is indicated between parentheses. Means with the same letter are not significantly
different at the 0.05 level based on Scheffe’s test. F, ANOVA; BW, birth weight; BL, birth length; W, weight; H, height; BMI, body mass index; AC/A,
arm circumference-for-age; TS/A, triceps skinfold-for-age; AMA/A, arm muscle area-for-age; AFA/A, arm fat area-for-age.
∗P<0.05.
∗∗ P<0.01.
∗∗∗∗ P<0.0001; (t), tendency (P<0.0927).
values <−2 S.D. had prenatal nutritional parameters such as
BW (P>0.05 and <0.1) and BL (P<0.05) representing
approximately 1000g and 5 cm less than the >2 S.D. group,
respectively. In children with Z-HC values <−2 S.D., 20%
had low BW (1.500 to <2.500g), 60% showed insufficient
BW (2.500–2.999g) and only 20%, showed values in the
range of 3.000–3.499g. In the group with Z-HC values >2
S.D., 33.3 and 66.7% weighted between 3.000 and 3.499g
and ≥3.500g at birth, respectively. In relation to BL, all the
children with Z-HC values <−2 S.D. had a length at birth
<50cm (40 and 60% presented BL values <48 cm and be-
tween 48 and 49cm, respectively). The opposite was ob-
served in the >2 S.D. group (33.3 and 66.7% registered BL
values between 50 and 51cm and ≥52 cm, respectively). As
regards present body W, values were significantly lower in
males with Z-HC values <−2 S.D. compared with the other
groups (P<0.01). In females, despite the fact that Wvalues
were lower in those with Z-HC values <−2 S.D., differences
were not significant. Present body Hwas lower in females
with Z-HC values <−2 S.D. (P<0.05) and, in males, H
did not differ significantly between Z-HC groups although
children with Z-HC values <−2 S.D. had a lower Hcom-
pared with the rest of the sample. Differences between HC
values (cm) expressed by Z-HC categories are obvious since
the sample was distributed on this basis. However, it is nec-
essary to underline that in males, HC values of school-age
children with Z-HC values <−2 S.D. were 8cm lower than
those with values >2 S.D.; in females, this difference was
5.6cm (P<0.0001). BMI (P<0.05), % AC/A(P<0.01)
and % AMA/A(P<0.05) values were significantly lower
in school-age children of the <−2 S.D. group. Antecedents
related to postnatal nutrition revealed that 16.6% of the sam-
ple had suffered from undernutrition in the first year of life
and that all of them belonged to low SES. Most children
with Z-HC values <−2 S.D. (60%) had suffered undernutri-
tion during the first year of life, a percentage that decreased
significantly to 20 and 6%, in the groups with Z-HC values
between −2 and <0 S.D. and 0–2 S.D., respectively (P<
0.05) (Fig. 2).
3.4. Parental HC by child’s Z-HC categories
Table 3 shows parental HC by child’s Z-HC categories.
Paternal and maternal absolute HC and Z-HC values were
1124 D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131
Fig. 2. Distribution of the sample of Chilean high school gradu-
ates with and without undernutrition in the first year of life by head
circumference-for-age Z-score (Z-HC) categories. χ2
o(1)=5.120 >
χ2
t(1)0.05 =3.841. Chi-square was calculated comparing Z-HC values
<0 and ≥0 S.D.
significantly lower in children with Z-HC values <−2 S.D.
compared with their peers from 0 to 2 S.D. and >2 S.D.
groups. Paternal and maternal Z-HC values were 2.83 and
1.76 points lower in children with Z-HC values <−2 S.D.
compared with >2 S.D. group, respectively.
3.5. Absolute parameters of brain development distributed
by Z-HC categories and sex
Table 4 outlines brain development parameters according
to Z-HC categories and sex. Males with Z-HC values <−2
S.D. registered a CC length significantly lower than the 0–2
S.D. group (P<0.05), an absolute BV significantly lower
than their peers of the other groups of the sample (P<
0.0001) and a APD significantly lower than their peers of
the 0–2 S.D. and >2 S.D. groups (P<0.0001). As a conse-
quence, the <−2 S.D. group had a BV 449.1 cm3lower than
the >2 S.D. group, while among the females this difference
was 214.4cm3(P<0.001). In males, APD was 13.2 mm
smaller in the <−2 S.D. group compared with their peers of
the >2 S.D. group (P<0.0001) while in the females this dif-
Table 3
Parental head circumference (HC) by school-age children’s HC-for-age Z-score (Z-HC) categoriesa
Parental HC Z-HC F
<−2 S.D. (5) −2to<0 S.D. (55) 0–2 S.D. (33) >2 S.D. (3)
Paternal
Absolute PHC (cm) 54.1a ±1.5 56.0ab ±1.3 56.6b ±1.0 57.9b ±1.0 6.54∗∗∗
Z-PHC −1.38a ±1.15 0.00ab ±0.95 0.50b ±0.78 1.45b ±0.71 6.51∗∗∗
Maternal
Absolute MHC (cm) 52.8a ±1.5 53.7ab ±1.5 54.6b ±1.1 55.2b ±0.9 4.15∗∗
Z-MHC −1.55a ±1.07 −0.87ab ±1.09 −0.26b ±0.80 0.21b ±0.67 4.14∗∗
aResults are expressed as mean ±S.D. The number of cases is indicated between parentheses. Means with the same letter are not significantly
different at the 0.05 level based on Scheffe’s test. F, ANOVA; PHC, paternal head circumference; Z-PHC, paternal head circumference-for-age Z-score;
MHC, maternal head circumference; Z-MHC, maternal head circumference-for-age Z-score.
∗∗ P<0.01.
∗∗∗ P<0.001.
ference was 18.5mm (P<0.01). CCGT, CCBT and CCST
values were not significantly different when evaluated in re-
lation to Z-HC in both males and females. Mean cortex thick-
ness in the frontal, parietal, temporal and occipital lobules
was near 4 mm in all Z-HC categories. Abnormal amounts of
white matter were detected in two children, five manifested
an abnormally large subarachnoid space and ventricular sys-
tem size and six of them had an encephalic parenchyma ab-
normal by MRI without significant association with Z-HC
categories.
3.6. Children BV and HC of the children and their parents
by total IQ group, SES and sex
Children BV, absolute HC and expressed as Z-HC both
in children and their parents by total IQ group, SES and sex
is indicated in Table 5. Within each IQ group no significant
difference was observed according to SES and sex for BV,
HC and Z-HC of the children and for HC and Z-HC of the
parents.
3.7. Interrelationship between most relevant parameters
Table 6 shows the Pearson correlation coefficients for the
most relevant parameters. A high correlation was observed
between Z-HC and absolute BV (r=0.841 P<0.0001).
Z-HC and BV were also positively and significantly cor-
related with parental HC, BW, BL, IQ, SA and AAT and
negatively with severe undernutrition in the first years of
life and age. All these variables were significantly interre-
lated. It should be noted that with the exception of under-
nutrition in the first year of life, age was negatively and
significantly correlated with all these variables. SES pos-
itively and significantly correlated with paternal HC and
BL and negatively with severe undernutrition in the first
year of life. No significant correlation was observed be-
tween BMI, AC/Aor AMA/Awith BW, BL, IQ, SA and
AAT.
D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131 1125
Table 4
Absolute brain development parameters of Chilean high school graduates by head circumference-for-age Z-score (Z-HC) categories and sexa
Absolute brain parameters Z-HC F
<−2 S.D. −2to<0 S.D. 0–2 S.D. >2 S.D.
Males (3) (24) (18) (2)
CC Length (mm) 64.7a ±4.0 70.9ab ±4.4 73.4b ±4.5 72.0ab ±2.8 3.66∗
CCGT (mm) 10.3 ±2.01 11.1 ±1.5 11.9 ±1.8 11.0 ±1.4 1.14
CCBT (mm) 6.0 ±1.0 6.1 ±1.0 6.4 ±0.7 5.5 ±0.7 1.03
CCST (mm) 10.0 ±2.0 11.4 ±1.3 11.7 ±1.8 10.0 ±0.0 1.68
BV (cm3) 1208.6a ±139.2 1432.9b ±87.4 1568.9c ±41.2 1657.7c ±7.7 28.25∗∗∗∗
BD (mm) 128.3 ±1.5 132.3 ±7.0 133.0 ±6.9 136.5 ±2.1 0.67
APD (mm) 156.3a ±10.1 161.9a ±5.3 169.1b ±4.4 169.5b ±6.4 9.24∗∗∗∗
Females (2) (31) (15) (1)
CC Length (mm) 69.0 ±0.0 70.5 ±4.6 71.1 ±5.5 79.0 ±0.0 1.11
CCGT (mm) 12.0 ±0.0 10.8 ±1.7 10.9 ±1.5 13.0 ±0.0 0.96
CCBT (mm) 6.0 ±0.7 6.4 ±0.8 6.7 ±0.9 6.0 ±0.0 0.49
CCST (mm) 10.5 ±2.1 11.2 ±1.5 11.6 ±1.7 13.0 ±0.0 0.75
BV (cm3) 1252.9a ±98.7 1370.8a ±77.5 1457.2b ±69.3 1467.3b ±0.0 7.11∗∗∗
BD (mm) 126.0 ±7.1 128.9 ±6.5 131.3 ±5.5 137.0 ±0.0 1.19
APD (mm) 155.5a ±0.7 160.3ab ±4.5 164.7ab ±6.0 174.0b ±0.0 5.74∗∗
aResults are expressed as mean ±S.D.The number of cases is indicated between parentheses. Means with the same letter are not significantly
different at the 0.05 level based on Scheffe’s test. F, ANOVA; CC, corpus callosum; CCGT, genu thickness; CCBT, body thickness; CCST, splenium
thickness; BV, brain volume; BD, biparietal diameter; APD, anteroposterior diameter.
∗P<0.05.
∗∗ P<0.01.
∗∗∗ P<0.001.
∗∗∗∗ P<0.0001.
3.8. Multiple regression analysis between children’s Z-HC
and most relevant parameters
Multiple regression analysis between children’s Z-HC
(dependent variable) and age, sex, SES, BV (absolute or ad-
justed), paternal and maternal HC, BL, BW and undernutri-
Table 5
Comparison between brain volume (BV) in children and head circumference (HC) of both children and their parents within each total intellectual quotient
(IQ) group by socio-economic stratum (SES) and sexa
High total IQ Student’s “t”-test Low total IQ Student’s “t”-test
High SES Low SES High SES Low SES
High school graduates
Males (n=47) (12) (11) (12) (12)
Absolute BV (cm3) 1551.6 ±63.7 1544.5 ±109.7 0.187 NS 1420.9 ±122.6 1409.5 ±129.3 0.222 NS
Absolute HC (cm) 56.6 ±1.4 56.4 ±1.5 0.232 NS 55.5 ±1.5 54.6 ±1.7 0.677 NS
Z-HC 0.59 ±1.04 0.54 ±1.12 0.100 NS −0.61 ±1.17 −0.94 ±1.29 0.652 NS
Females (n=49) (12) (11) (12) (12)
Absolute BV (cm3) 1434.2 ±86.7 1403.4 ±99.1 0.809 NS 1381.2 ±96.8 1361.4 ±64.0 0.600 NS
Absolute HC (cm) 55.1 ±1.1 54.5 ±1.2 1.258 NS 54.5 ±1.2 53.7 ±1.0 1.692 NS
Z-HC 0.19 ±0.80 −0.20 ±0.87 0.151 NS −0.34 ±1.07 −0.89 ±0.76 1.445 NS
Parents
Absolute PHC (cm) 56.8 ±1.2 56.2 ±1.2 1.515 NS 56.4 ±1.5 55.4 ±1.4 1.678 NS
Z-PHC 0.63 ±0.86 0.20 ±0.88 1.517 NS 0.34 ±1.10 −0.37 ±1.00 1.676 NS
Absolute MHC (cm) 54.2 ±1.4 54.1 ±0.9 0.319 NS 53.8 ±1.5 54.0 ±1.9 0.484 NS
Z-MHC −0.52 ±1.02 −0.61 ±0.67 0.313 NS −0.84 ±1.05 −0.67 ±1.35 0.482 NS
aResults are expressed as mean±S.D.The number of cases is indicated between parentheses. Z-HC, school-age children’s head circumference-for-age
Z-score; PHC, paternal head circumference; Z-PHC, paternal head circumference-for-age Z-score; MHC, maternal head circumference; Z-MHC, maternal
head circumference-for-age Z-score; NS, not significantly different.
tion in the first year of life (independent variables) (Table 7)
confirms that absolute BV (or adjusted) (P<0.0001), pa-
ternal HC (P<0.0116), maternal HC (P<0.0200) and
BL (P<0.0364) were the independent variables with the
greatest explanatory power for children’s Z-HC variance
(r2=0.727) without interaction with age, sex or SES.
1126 D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131
Table 6
Correlation matrix. Pearson correlation coefficients for the most relevant parametersa
Z-HC BV Z-PHC Z-MHC BW BL UN IQ SA AAT AGE SES
Z-HC –
BV 0.841∗∗∗∗ –
Z-PHC 0.546∗∗∗∗ 0.415∗∗∗ –
Z-MHC 0.407∗∗∗∗ 0.290∗∗ 0.120 NS –
BW 0.289∗∗ 0.229∗0.278∗0.034 NS –
BL 0.325∗∗ 0.256∗0.200 NS 0.011 NS 0.646∗∗∗∗ –
UN −0.370∗∗∗ −0.327∗∗ −0.338∗∗ −0.163 NS −0.390∗∗∗∗ −0.476∗∗∗∗ –
IQ 0.465∗∗∗∗ 0.436∗∗∗∗ 0.292∗0.121 NS 0.258∗0.287∗−0.557∗∗∗∗ –
SA 0.450∗∗∗∗ 0.451∗∗∗∗ 0.330∗∗ 0.097 NS 0.367∗∗∗ 0.354∗−0.469∗∗∗∗ 0.893∗∗∗∗ –
AAT 0.434∗∗∗∗ 0.321∗∗ 0.225 (t) 0.108 NS 0.189 (t) 0.228 (t) −0.419∗∗∗∗ 0.923∗∗∗∗ 0.925∗∗∗∗ –
AGE −0.342∗∗∗ −0.282∗∗ −0.346∗∗∗ −0.013 NS −0.252∗−0.414∗∗∗ 0.454∗∗∗∗ −0.490∗∗∗∗ −0.563∗∗∗∗ −0.422∗∗∗∗ –
SES 0.158 NS 0.091 NS 0.297∗0.021 NS 0.188 (t) 0.234∗−0.447∗∗∗∗ 0.155 NS 0.001 NS 0.012 NS 0.074 NS –
aZ-HC, school-age children’s head circumference-for-age Z-score; BV, brain volume; Z-PHC, paternal head circumference-for-age Z-score; Z-MHC, maternal head circumference-for-age Z-score; BW,
birth weight; BL, birth length; UN, severe undernutrition in the first year of life; IQ, intellectual quotient; SA, scholastic achievement; AAT, academic aptitude test; SES, socio-economic stratum. (t)
=tendency (P> 0.05 and <0.10); NS, not significantly different.
∗P<0.05.
∗∗ P<0.01.
∗∗∗ P<0.001.
∗∗∗∗ P<0.0001.
D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131 1127
Table 7
Multiple regression analysis table (Statistical Analysis System: PROC GLM Error type III) between school-age children’s head circumference-for-age
Z-score (Z-HC) (dependent variable) and most relevant parameters (independent variables)a
Parameter Estimate Tfor HO:
parameter =0Pr > [T] Standard error
of estimate Partial r2% of the explained
variance
Intercept −12.58293588 3.33 0.0017 3.78328904
Age −0.02773961 0.19 0.8514 0.14731657 – –
Sex ––
Males −0.09790286 0.44 0.6643 0.22417235 –
Females 0.00000000 – – – –
SES ––
High −0.11033484 0.54 0.5933 0.20520157 –
Low 0.00000000 – – – –
UN ––
Yes 0.21685458 0.60 0.5525 0.36245650 –
No 0.00000000 – – – –
Absolute BV 0.00563919 5.04 0.0001 0.00111827 0.568 78.1
Z-PHC 0.28188927 2.63 0.0116 0.10735975 0.075 10.3
Z-MHC 0.22542435 2.41 0.0200 0.09364889 0.042 5.8
BL 0.08834878 2.15 0.0364 0.04104100 0.042 5.8
BW 0.00018131 0.80 0.4256 0.00022562 – –
aModel r2=0.727; root MSE, (standard deviation of the dependent variable)=0.65460692; model F-value =14.19; P<0.0001. SES, socio-economic
stratum; UN, severe undernutrition in the first year of life; BV, brain volume; Z-PHC, paternal head circumference-for-age Z-score; Z-MHC, maternal
head circumference-for-age Z-score; BL, birth length; BW, birth weight.
4. Discussion
The findings of the present study reveal that independent
of age, sex and SES, BV is the most relevant independent
variable explaining head size (78.1% of the explained vari-
ance) entering in the first place in the statistical regression
model, followed by paternal and maternal Z-HC and child’s
BL. The high correlation registered between Z-HC-BV is
similar to that reported for infants and children and con-
firms that HC is the anthropometric indicator both brain
development and nutritional background (Bartholomeusz,
Courchesne, & Karns, 2002;Vernon et al., 2000). At birth,
the human brain is at 25% of its adult volume. It reaches
75% of its adult volume by the end of the first postnatal
year. The remaining 25% of brain growth is achieved over
the next few years of life (Reynolds, Johnston, Dodge,
DeKosky, & Ganguli, 1999). In this context, our results
confirm that brain growth is the principal determinant of
the growth of the cranial vault, which normally ceases to
grow at about 7 years of age; thus, adult head size is a
reasonable measure of brain size at the end of brain growth
(Reynolds et al., 1999). As a result, in the sample analysed
in the present study, the decreased head circumference is
proportional to the brain volume. In our study, HC and
BV registered a positive and significant correlation with
parental Z-HC, BW, BL, IQ, SA and AAT and negatively
with undernutrition during the first year of life and age.
Paternal and maternal Z-HC entered in second and third
place in the statistical regression model, respectively (10.3
and 5.8% of the explained HC variance). In twins, their
spouses, and their children, parental HC has been described
explaining approximately half the normal variation in head
size and, at least in part, presumably genetically determined
(Weaver & Christian, 1980).
Children’s BL was the only anthropometric index of pre-
natal nutrition that entered in the statistical regression model
in fourth place explaining 5.8% of Z-HC variance. The high-
est correlation for BL was found with undernutrition in the
first year of life and this could be and indicator of the impact
of a more deprived nutritional background on HC. In con-
sequence, the magnitude of the decrease of HC is a reliable
indicator of the severity of nutritional deprivation (Winick
& Rosso, 1969a,b). HC has been recognised as the most
sensitive anthropometric index of prolonged undernutrition
during infancy, associated with intellectual impairment es-
pecially verbal IQ, such as in our study (Ivanovic et al.,
2000d; Leiva et al., 2001; Stoch et al., 1982). Undernutrition
was significantly more prevalent in children with a low HC
(<−2 S.D.) who presented the lowest verbal IQ, BV and
APD values and this latter finding is especially outstanding
since APD involves language and visualisation areas (Stoch
et al., 1982; Willerman et al., 1991); this could explain that
in children with low HC (<−2 S.D.), verbal skills are more
deteriorated than non-verbal skills (Ivanovic et al., 2000d;
Stoch et al., 1982). In children with HC >2 S.D., verbal IQ
was higher than nonverbal IQ and in groups with a “normal
HC “ (mean±2S.D.) similar values were found between to-
tal, verbal and non-verbal IQ although IQ was significantly
higher in 0–2 S.D. group. Findings from several studies
emphasise that among preschoolers HC might reflect better
than body Hthe impact of nutritional deficiencies at an
early age; this measurement is useful in the identification of
the period during which malnutrition occurred (Johnston &
Lampl, 1984;Malina et al., 1975). Currently, undernutrition
1128 D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131
constitutes the most important nutritional problem in devel-
oping countries where, at an early age, it affects the growth
and development of children and may alter brain develop-
ment and intelligence; poverty and deprivation exacerbate
these negative effects, especially when mothers have poor
schooling and low intellectual levels (Brown & Pollitt, 1996;
Ivanovic et al., 2000d, 2002; Leiva et al., 2001;Pollitt,
2002). In the high SES, the prevalence of undernutrition
is very low and it does not occur generally during the first
year of life (Ivanovic, Olivares, & Ivanovic, 1991). When
mothers of the high SES work and their children are cared
for by domestic personnel, malnutrition may result from
lack of proper nutrition, but despite of this, the effects of
undernutrition are ameliorated by the better socio-economic
conditions and stimulation. Even more, if mothers have
anorexia nervosa their children can suffer from undernutri-
tion, they are more likely to be born prematurely with lower
birth weight and length and with serious difficulties in main-
taining breastfeeding (Waugh & Bulik, 1999). Furthermore,
mothers from the high SES with low IQ have infants with
a lowered HC, BV, IQ, SA and AAT, similarly to low SES
infants from mothers with low IQ (Ivanovic et al., 2002).
Malnutrition has been associated with retarded BW, BL, HC,
BV, APD, IQ, SA, AAT and altered functional development
that persist into adult life (Brown & Pollitt, 1996;FAO,
1996; Fattal-Valevski et al., 1999;Grantham-McGregor &
Fernald, 1997;Ivanovic, 1996; Ivanovic et al., 2000d, 2002;
Leiva et al., 2001; Stoch et al., 1982; UNICEF, 2003;Winick
& Rosso, 1969a,b). It is important to underline that these
interrelationships do not have direct cause-effect relation-
ships since complex interactions are established between
them (Brown & Pollitt, 1996); SES is co-determinant of
nutritional status, HC, brain development, IQ, SA and AAT.
The results of this study demonstrate that children with a
low HC had a decreased BV, APD, BW, BL, BMI, % AC/A,
% AMA/A, IQ, SA and AAT and parental HC, and higher
incidence of undernutrition in the first year of life, com-
pared with their peers with higher Z-HC values, even when
considering that these children were significantly younger
than the former. Our findings reveal that HC is positively
and significantly correlated with indicators of prenatal nu-
trition like BW and BL and children with a low HC also had
a more negative postnatal nutritional environment, a more
deteriorated current nutritional status, lower IQ and learn-
ing problems (Larroque, Bertrais, Czernichow, & Leger,
2001;Lundgren, Cnattingius, Jonsson, & Tuvemo, 2001;
Markestad et al., 1997; Matte et al., 2001;Nelson,
Goldenberg, Hoffman, & Cliver, 1997;Sorensen et al.,
1999).
The impact of genetic factors should be taken into consid-
eration since parental HC contributed to explain the child’s
HC variance and these factors also affect HC, BV and IQ.
It is possible that other environmental and genetic factors,
which were not evaluated, could affect BW, BL, HC, BV, IQ,
SA and AAT. It has been shown that genetic factors affect
the growth of children with very low BW, HC, BV and IQ
and, as we stated previously, it has been estimated that addi-
tive genetic effects probably explain half of the phenotypic
variance for HC and IQ (Bouchard, 1998; Casto et al., 1995;
McGue & Bouchard, 1998;Strauss & Dietz, 1998;Weaver &
Christian, 1980). The intrauterine environment could play a
substantial role in the early stages of physical development in
children in relation to W,Hand HC, but further analyses are
needed to clarify how this environment affects child growth
and for how long (Livshits, Peter, Vainder, & Hauspie, 2000).
Several authors have shown a positive and significant cor-
relation among HC, BV and IQ and it seems likely that in
the general population the true correlation between HC-IQ
and between BV-IQ would be no less than 0.40 and equally
strong in males and in females, which is in agreement
with our results (Botting et al., 1998; Dolk, 1991;Gignac,
Vernon, & Wickett, 2002;Ivanovic et al., 2000a,b,d;Nelson
& Deutschberger, 1970;Ounsted et al., 1988; Reiss et al.,
1996; Rushton, 2000;Strauss & Dietz, 1998;Vernon et al.,
2000;Wickett, Vernon, & Lee, 1994;Willerman et al., 1991).
In this manner, most studies confirm that a lowered HC or
BV was associated with low IQ, SA and AAT (Bouchard,
1998; Desch et al., 1990; Gignac et al., 2002;Ivanovic et al.,
2000a,b,d, 2002;Reynolds et al., 1999; Rushton, 2000;
Vernon et al., 2000;Weinberg, Dietz, Penick, & McAlister,
1974); however, other authors did not find significant dif-
ferences between the mean IQs of the study subjects (HC
<−2 S.D.) and normal controls, although mean academic
achievement scores were significantly lower in the former
(Przytycki & Burgin, 1992;Sells, 1977). Other investigators
found that non-organic failure to thrive in infancy is fol-
lowed by persistent stunting and wasting and a reduced HC
but it is not associated with cognitive or educational disad-
vantages at school age (Drewett, Corbett, & Wright, 1999).
In the present study, despite the fact that very few cases
were found in the extreme categories for Z-HC, the dis-
tribution of the sample in the different Z-HC categories is
similar to that found in the 1986–1987 survey carried out in
a representative sample of 4509 school-age children from
elementary and high schools, in Chile’s Metropolitan Re-
gion (Ivanovic et al., 1995). This may indicate that indepen-
dently of sample size, the distribution of children according
to Z-HC categories in the Chilean school-age population is
similar. Even more, 100.0 and 60.0% of school-age children
with Z-HC values <−2 S.D. (microcephaly) and between
−2 and <0 S.D., respectively, had low IQ; the opposite
was observed for categories 0–2 S.D. and >2 S.D. (macro-
cephaly) in which 66.7 and 100%, respectively, exhibited
high IQ (Fig. 1). Therefore, we emphasise that a “normal”
HC (mean ±2S.D.) is more related to statistical normality,
although this may not be so for psychological or educa-
tional achievements. In fact, HC values below the mean
are associated with an increased incidence of lower IQ;
this means that small differences in HC could be of con-
siderable importance in the interrelationship between HC
and IQ (Ivanovic et al., 2000b). The findings of this study
should be considered as statistical associations and do not
D.M. Ivanovic et al./Neuropsychologia 42 (2004) 1118–1131 1129
represent a cause-and-effect relationship. This is a matter in
which further research should be carried out. In the present
study, as already stated, very few cases in the extreme cat-
egories of Z-HC were found, but despite of this, results are
significant and eloquent and are in agreement with our pre-
vious findings in larger samples (Ivanovic et al., 1995). The
results of the present study demonstrate that HC and BV
values are related more to differences in IQ and not to SES
conditions.
“Primary” microcephaly means an abnormal HC at
birth and “secondary” microcephaly a normal HC at birth,
with microcephaly acquired later due to deceleration of
brain growth reflecting infections, trauma, intoxications,
metabolic diseases, the Rett syndrome, or central nervous
system degenerative diseases (Opiz & Holt, 1990). On
the other side, primary megalencephaly at birth had been
described as a risk factor for low levels of intelligence
but not for visual and auditive impairments (Petersson,
Pedersen, Schalling, & Lavebratt, 1999). Based on the re-
sults of this study and on our previous findings we contend
that, at school age, children with HC values <−2 and >2
S.D. had low and high IQ, respectively. In this sample of
children, mothers reported that their children had never
been diagnosed as being microcephalic or macrocephalic
and for this reason the etiologies remain unknown.
Some authors have pointed out that larger brains have
more neurons and therefore, a greater number of synaptic
connections, which may mean a higher IQ (Pakkenberg &
Gundersen, 1997). It has been suggested that individual dif-
ferences in myelination, which affects neural transmission
rates, may be the basis for the HC–BV–IQ correlation al-
though there is a low correlation between neural speed and
mental speed, suggesting that other mechanisms must be in-
volved (Miller, 1994; Tan, 1996; Vargas et al., 2000;Wickett
& Vernon, 1994). Delayed myelination and abnormalities in
neuron migration have been described as the most predom-
inant disorders in children with associated neurologic find-
ings, whereas focal white matter lesions were more common
in children without neurologic symptoms; these alterations
were significantly more common in children with a small
HC (Kjos, Umansky, & Barkovich, 1990).
This multifactorial study provides substantial evidence
at 18 years of age for associations between head size and
measures of brain size, nutritional status, IQ, learning and
parental HC. These findings confirm the hypothesis that:
(1) independently of age, sex and SES, brain parameters,
parental HC and prenatal nutritional indicators are the most
important independent variables that determine children’s
HC and (2) microcephalic children (Z-HC <−2 S.D.)
present multiple disorders not only related to BV but also
to IQ, SA and nutritional background, variables that are
significantly interrelated. These results may be useful for
the understanding of human behaviour related to disorders
of intellectual and learning achievements when children
present with decreased HC resulting from a lower brain size
and a deprived nutritional background, especially prenatally.
Acknowledgements
The authors are very gratefully to the Ministry of Edu-
cation of Chile for all the facilities given to carry out this
research; to Dr. Oscar Brunser MD, for helpful comments
and suggestions. Supported in part by Grant 1961032 from
the National Fund for Scientific and Technologic Develop-
ment (FONDECYT), Grant 024/1997 from the University
of Chile, Postgradest Department and Grant SOC 01/13-2
from the Research Department (DI), University of Chile.
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