- Access to this full-text is provided by Springer Nature.
- Learn more
Download available
Content available from Scientific Reports
This content is subject to copyright. Terms and conditions apply.
1
Vol.:(0123456789)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports
Brain structural parameters
correlate with University Selection
Test outcomes in Chilean high
school graduates
Daniza Ivanovic
1,2*, Francisco Zamorano
3, Patricia Soto‑Icaza
2, Tatiana Rojas
1,
Cristián Larraín
4, Claudio Silva
4, Atilio Almagià
5, Claudia Bustamante
1, Violeta Arancibia
6,
Francisca Villagrán
1, Rodrigo Valenzuela
7, Cynthia Barrera
7 & Pablo Billeke
2*
How well students learn and perform in academic contexts is a focus of interest for the students, their
families, and the entire educational system. Although evidence has shown that several neurobiological
factors are involved in scholastic achievement (SA), specic brain measures associated with academic
outcomes and whether such associations are independent of other factors remain unclear. This study
attempts to identify the relationship between brain structural parameters, and the Chilean national
University Selection Test (PSU) results in high school graduates within a multidimensional approach
that considers socio‑economic, intellectual, nutritional, and demographic variables. To this end, the
brain morphology of a sample of 102 students who took the PSU test was estimated using Magnetic
Resonance Imaging. Anthropometric parameters, intellectual ability (IA), and socioeconomic status
(SES) were also measured. The results revealed that, independently of sex, IA, gray matter volume,
right inferior frontal gyrus thickness, and SES were signicantly associated with SA. These ndings
highlight the role of nutrition, health, and socioeconomic variables in academic success.
e learning process is a multidimensional issue that depends on several elements related to the child, the
families, and the educational system1–3. Studies about scholastic achievement (SA) have shown that several
neurobiological factors impact academic performance. Nonetheless, specic brain measures that independently
inuence the SA have not been suciently investigated in school-age students. is fact is especially relevant in
the analysis of the University Selection Test (PSU, Prueba de Selección Universitaria), the national baccalaureate
examination for admission to Chilean universities, which has obvious implications for the future of the students
as a result of ranking by score.
e intellectual ability (IA)4 is the most studied and relevant factor that impacts SA5–7. Several studies have
highlighted the association between IA and brain structures8–12. Most research indicates that gray matter volume
(GMV), rather than white matter (WM), correlates with IA12. During a child’s development, brain volume and
head circumference (HC) also positively correlate with IA13,14. Other ndings distinguish dierential contri-
butions of GMV and WM microstructure connections to individual dierences in intelligence and memory,
respectively15. While GMV correlates with IA8,9,12, the relationship between GMV and specic cognitive abilities
is not straightforward. For example, studying a large Magnetic Resonance Imaging (MRI) sample of school-age
students, research has found only a signicant correlation between GMV and single-word reading in adolescents
separated by sex16. us, the weight of specic cognitive function and whole brain functioning in the relationship
between GMV and IA is unclear.
OPEN
1Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food
Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile. 2Laboratorio de
Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad
de Gobierno, Universidad del Desarrollo, Santiago, Chile. 3Unidad de Imágenes Cuantitativas Avanzadas,
Departamento de Imágenes, Clínica Alemana, Universidad del Desarrollo, Santiago, Chile. 4Radiology Department,
Facultad de Medicina-Clínica Alemana, Universidad del Desarrollo, Santiago, Chile. 5Laboratory of Physical
Anthropology and Human Anatomy, Institute of Biology, Faculty of Sciences, Ponticia Universidad Católica de
Valparaíso, Valparaíso, Chile. 6Department of Global Partnership for Education (GPE) World Bank, Washington,
USA. 7Department of Nutrition, Faculty of Medicine, University of Chile, Santiago, Chile. *email: islabrac02@
gmail.com; pbilleke@udd.cl
Content courtesy of Springer Nature, terms of use apply. Rights reserved
2
Vol:.(1234567890)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
Despite the preceding ndings, the neurobiological factor underlying SA has just begun to be studied. e
evidence has noted that HC and brain size correlate with SA17–20. Our prior research also indicated that broad
brain volume measures correlate with SA5. A recent study shows that cortical thickness can accurately classify
individuals with high and low SA21. Furthermore, functional connectivity of some brain areas, including the
inferior frontal cortex, correlates with SA22. However, when IA is considered in statistical modeling, the broad
brain measure loses an association with SA. e preceding research data demonstrates that more precise meas-
urements of brain morphometry are needed in order to arm an association with SA.
Another relevant factor that impacts SA is an early averse social environment, which disturbs brain matura-
tion with potential implications for mental health23. For instance, malnutrition alters HC, brain development,
and intelligence; poverty and deprivation exacerbate these adverse eects that persist at least into childhood
and adolescence24–31. us, early postnatal nutrition is essential for brain growth and maturation, impacting
WM connectivity and long-term cognitive functions32,33. Several authors have emphasized the importance of
particular omega-3 polyunsaturated fatty acid patterns on SA, IA, and brain structural volumes34–36. Along this
same line of research, low socioeconomic status (SES) and the experience of traumatic, stressful events impact
brain development37,38. Accordingly, both early life and current SES signicantly help to explain the variance of
gray matter39,40.
Overall, the evidence suggests that brain volume is associated with SA. Nonetheless, the role of specic brain
areas in this relationship is unclear, as is whether this association is independent of other factors, such as IA or
nutritional status. Hence, this study aimed to relate the brain structural parameters and the results of the PSU
in Chilean high school graduates within the framework of a multidimensional approach considering socio-
economic, intellectual, nutritional, and demographic variables. e purpose was to test the hypotheses that (i)
brain parameters such as GMV independently correlate with PSU scores and that (ii) IA, GMV, SES, and sex are
the most relevant parameters that explain PSU outcomes variance.
Methods
Design. is is an observational, cross-sectional, and comparative study.
Description of the population. e target population, 96,197 students (39% of the Chilean school popu-
lation), included all school-age participants enrolled in the rst grade of high school (HSG) in the urban area
of the Metropolitan Region of Chile in 2010 (Chile, Ministerio de Educación, 2009). ey belonged to the pub-
lic, private-subsidized, and private non-subsidized schools and were distributed in 1,262 educational establish-
ments, as was described in previous studies1,2.
Description of the sample. e sampling plan was widely described in our previous studies1,2. A repre-
sentative sample of 671 school-age students of the 2010 rst HSG and their parents, the school principals, and
teachers agreed to participate and signed the informed consent form. At the end of 2013, the students of the 2010
rst HSG graduated from the fourth HSG and took the PSU. A total of 550 and 548 school-age participants took
the language and mathematics PSU tests, respectively. All the school-age students (n = 160) who obtained high
(n = 91) or low PSU scores (n = 69) in both language and mathematics were invited to participate in the study. A
high PSU score was dened as greater than 620 in both tests, representing the 75th percentile at both the sample
and national levels. In contrast, a low PSU score was dened as less than 450 in both tests, representing the 25th
percentile at both the sample and national levels. Note that the PSU score is a normalized scale with a mean of
500, a standard deviation of 110, a minimum score of 150, and a maximum of 850. A total of 102 healthy high
school graduate students born at term voluntarily agreed to participate and signed the informed consent form.
All of them were successfully scanned by MRI. All participants had no history of alcoholism, neuropsychiatric
diagnosis, symptoms of brain damage, intrapartum fetal asphyxia, hyperbilirubinemia, epilepsy, or heart disease,
and their mothers had no history of smoking, alcoholism, or drug intake before and during pregnancy. Partici-
pants’ age ranged from 17.3 y to 20.3 y (mean age = 18.2 ± 0.5 y). In the High SA Group, 75.8% of the high PSU
scores were obtained by males, and in the Low SA Group, 65.2% of the low PSU scores were obtained by females
(p < 0.0001). Figure1 shows the ow diagram of the sample selection and distribution by group and sex.
Brain structural parameters: data acquisition. Images were acquired at the Radiology Department of
the Clínica Alemana de Santiago with a 3T Siemens Skyra scanner and a 20-channel head coil. Participants were
prepared for the MRI and were instructed to relax and keep still during image acquisition. For each subject, a 3D
structural T1-weighted scan [voxel size, 1 × 1 × 1mm; slices per slab, 176; eld of view (FoV), 256mm; repetition
time (TR) = 2.53s; echo time (TE) = 2.19ms]. Cortical and subcortical segmentation and cortical thickness were
obtained using FreeSurfer 6 (http:// surfer. nmr. mgh. harva rd. edu) methods using volumetric T1 imaging41–43.
Cortical thickness, dened as the shortest distance between the gray-white matter boundary and the outer corti-
cal boundary, was measured at each vertex across the surface. Cortical thickness surface maps were smoothed
with a Gaussian kernel of full width at a half-maximum of 10mm.
SES. SES was measured using the Graar Modied Scale adapted for Chilean urban and rural populations,
which considers the following socioeconomic indicators: parental schooling and occupation of the household
head and the housing characteristics (building materials, ownership, water supply, and ownership of durable
goods)44. Specically, the data of SES was obtained through an interview with the student’s mother. A six-point
scale was obtained as follows: High SES = 1, 2 points; Middle SES = 3 points; Low SES = 4, 5 points; Extreme
poverty SES = 6 points.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
3
Vol.:(0123456789)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
IA. IA was assessed with the standard version of the Raven’s Progressive Matrices Test (RPMT) in book
form, with a general scale for children of 12years and above that had been standardized for Chilean school-
age students19,45. e Standard RPMT is a non-verbal test and, in any of its forms, constitutes one of the tests
most frequently applied for quantication of general intelligence, evidencing a robust and reliable measure of
the general intelligence factor46. e test was administered collectively in the classrooms by an educational psy-
chologist. WHO experts for developing countries have recommended applying Raven’s test because its results are
not aected by culture31. Scores were registered as a percentile scale according to age, in the following grading:
Grade I = Superior Intellectual Ability (score ≥ p95); Grade II = Above Average (score ≥ p75 and < p95); Grade
III = Average (score > p25 and < p75); Grade IV = Below Average (score > p5 and ≤ p25) and Grade V = Intellectu-
ally Decient (score ≤ p5). For the analyses, IA grades were pooled into two groups: I + II and III + IV + V. e
rationale aimed to obtain two groups of participants as equitably and balanced as possible to estimate statistical
parameters, that is, to obtain no more than the 20% of the cells having smaller amounts than 5.
Nutritional status. e prenatal nutritional background and early nutritional measurements, such as birth
weight, body length, and duration of breastfeeding, were registered. Measurements of weight (W), height (H),
and head circumference (HC) were carried out at school using standardized procedures (Gibson, 1990). e
postnatal nutritional background was expressed as height-for-age Z-score (Z-H) according to NCHS-CDC
tables47. e head circumference-for-age Z-score (Z-HC) was assessed using tables48–50. Z-HC values were simi-
lar when applying these tables (the correlation coecient between these patterns was 0.98). Finally, Z-HC values
were calculated using the tables of Roche etal.50. e current nutritional status was expressed as body mass index
(BMI, weight/height2, NCHS-CDC tables48,49). BMI values are commonly categorized as follows: underweight
(BMI < 18.5), healthy weight (BMI between 18.5 to < 25), overweight (BMI is 25.0 to < 30), and obesity (BMI is
30.0 or high), although in the present study values were expressed as mean ± SD. Higher BMI values are related
to a high proportion of body fat and, as a result, poor nutritional status. Note that, in the current sample, only
2 participants had BMI < 18.5. BMI was calculated using biological age derived from the Tanner stages. Birth
weight and birth length were used as indices of prenatal nutrition, Z-H and Z-HC served as indicators of post-
natal nutritional background, and Z-BMI was used as an index of current nutritional status.
Figure1. Flow diagram of the sample selection and distribution of the sample by group and sex. PSU:
University Selection Test; 1HSG: the rst high school grade; 4HSG: the fourth high school grade; SA: scholastic
achievement.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
4
Vol:.(1234567890)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
PSU. Results from the PSU outcomes in language and mathematics tests were registered for the 2010 rst
HSG school-age students when they graduated from the fourth HSG in 2013. PSU has a maximum score of 850
and a minimum of 150 for each test (language and mathematics tests with 80 items each) and was expressed as
mean ± SD. Scores below 450bar students from applying to universities. PSU was considered a dependent vari-
able.
Statistical analysis. Data were analyzed using analysis of variance (ANOVA) and t-test for comparison of
means aer applying the Shapiro–Wilk test to establish whether the distribution of the variables was normal.
Multiple comparisons were corrected by Bonferroni’s test. Non-parametric tests (chi-square) were used for cat-
egorical variables. Pearson correlation coecient was used to establish interrelationships between variables. Par-
tial correlations were used to control for the interdependence of dierent brain volumes within subjects, as has
been proposed and used for structural brain data51–53. e correlation values with brain structural parameters
were corrected by the false discovery rate (FDR, q < 0.05). e determination coecient (R2) was calculated to
measure the t of the regression models. Pearson and Spearman correlation coecients were used for quanti-
tative and ordinal variables, respectively. e stepwise procedure was used in the linear regression analysis to
establish the most important independent variables aecting PSU (mean language + mathematics), language,
and mathematics scores (dependent variable). e brain parameter initially evaluated for the selection method
involved all those structures with PSU (language or mathematics) correlation greater than 0.5 (abs(r) > 0.5). For
all hypothesis tests, the level of signicance was < 0.05 two-tails. All the comparisons were carried out separately
by sex, except when sex was included as an independent regressor (Whole-brain analysis and general linear
model) and in the PSU score in the demographic descriptions (Table1). Note that in the preceding case, the
between-group comparison does not have relevance because the PSU score was the selection criteria for the
group selection.
Data were processed using the Statistical Analysis System package (SAS 9.3, SAS Institute Inc. (Cary, NC).
Whole-brain analyses across the cortical surface vertex were performed to reduce the risk of Type II errors.
ese analyses were carried out using Surfstat (http:// www. math. mcgill. ca/ keith/ surfs tat/), a toolbox created
for MATLAB (e MathWorks, Inc., Nathan, MA). Random eld theory (RFT) corrections (cluster corrected
p < 0.05, cluster threshold detection, CTD, p < 0.001) were used to account for multiple comparisons54. In order
to incorporate the results from the whole-brain analyses of the cortical surface, the right inferior frontal gyrus
and the le inferior frontal gyrus (for completeness) volumes were extracted using an independent ROI from
the area A45c_r and A45c_l of the Brainnetome atlas (https:// atlas. brain netome. org/)55.
Ethical approval and consent to participate. e experimental protocol and all methods were per-
formed in accordance with institutional guidelines and were approved by the Ethics Committee in Studies in
Humans of the Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), Uni-
versity of Chile, and ratied by the Bioethics Committee of the National Fund for Scientic and Technological
Table 1. Mean age, University Selection Test scores, socioeconomic and intellectual variables by group and
sex. Data are expressed as mean ± SD, Means were compared by Bonferroni’s test; Data for SES are expressed
in percentage of cases for SES categories and compared by Chi-square test. Data for IA are expressed in
percentage of cases for IA categories and compared by Chi-square test. IA gradeswere pooled in two groups:
I + II, and III + IV + V. IA grades: Grade I, superior; Grade II, above average; Grade III, average; Grade IV, below
average; Grade V, intellectually decient. p < .05 *; < .01**; < .001 *** # SA: scholastic achievement; High SA
Group: High PSU score (> 620, > p75); Low SA Group: Low PSU score (< 450, < p25).
Variables
Males Females Within group comparison
High SA
Group#
(n = 42)
Low SA
Group#
(n = 16) p-value
High SA
Group#
(n = 21)
Low SA
Group#
(n = 23) p-value
High SA
Group#
p-value
Low SA
Group#
p-value
Demographic
Age (y) 18.1 ± 0.3 18.6 ± 0.8 .0003*** 18.0 ± 0.3 18.1 ± 0.5 .3582 .4795 .0292*
University Selection Test (PSU) scores
PSU (L + M) 712 ± 41 395 ± 56 672 ± 42 388 ± 55 .0005*** .7209
Language (L) 690 ± 50 403 ± 72 665 ± 53 388 ± 73 .0761 .5317
Mathematics (M) 734 ± 61 390 ± 83 677 ± 69 388 ± 68 .0013** .9392
Socioeconomic status (SES)
SES
(high + medium) 81.0 56.2 84.2 13.0 Xo2 = 34.0038
df = 3
p < .0001***
SES (low) 19.0 43.6 15.8 87.0
Intellectual ability (IA)
IA (I + II) 97.6 18.8 90.5 4.4 Xo2 = 75.5633
df = 3
p < .0001***
IA (III) 2.4 56.2 9.5 60.8
IA (IV + V) 0.0 25.0 0.0 34.8
Content courtesy of Springer Nature, terms of use apply. Rights reserved
5
Vol.:(0123456789)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
Development (FONDECYT), Chile. e participants’ informed consent was obtained according to the norms
for Human Experimentation, Code of Ethics of the World Medical Association (Declaration of Helsinki).
Results
Sample. From a representative cohort of school-age students1,2,35, a sample of 102 participants was success-
fully scanned aer they completed the University Selection Test (PSU). is sample represents 69% and 56% of
students who obtained the highest (High SA Group) or the lowest (Low SA Group) PSU scores, respectively (see
Methods for further details). e demographic descriptions of the sample are shown in Table1. Menarcheal age
did not dier signicantly between females in High SA (12.6 ± 1.2) and Low SA groups (12.5 ± 1.2) (F = 0.12;
p = 0.7359). Males from the High SA Group obtained higher scores in the PSU than females from the same group
(p = 0.0005), which is explained by their higher scores in mathematics (p = 0.0013).
Comparison of family SES. A signicant dierence among SES categories by sex and group was found
(Xo2 = 34.0038; df = 3; p < 0.0001). In the High SA Group, most participants, 81% and 84.2% of males and females,
respectively, belonged to the high and medium SES. While in the Low SA Group, the percentage of participants
that belonged to the high and medium SES was 56.2% and 13% for males and females, respectively. To note, most
females of the Low SA Group belonged to low SES (87%) (Table1). e parents and the head of the household
of the school-age children from the High SA Group had higher levels of schooling and jobs and lived in better
housing quality than their peers with low SA (p < 0.0001). SES, as well as socioeconomic indicators (schooling
and occupation of the household head and the housing characteristics), were positively and signicantly associ-
ated with PSU outcomes both in the language and mathematics tests (p < 0.0001).
IA. IA was estimated through Raven’s Progressive Matrices Test (see "Methods"). A signicant dierence
among AI categories by sex and group was found (Xo2 = 75.5633; df = 3; p < 0.0001; detailed results are shown in
Table1). To note, a great percentage of participants of the High SA Group exhibit an IA in the categories I + II
(97.6% and 90.5% for males and females, respectively). However, in the Low SA Group, males and females reg-
istered mainly IA grade III, followed by grades IV + V.
Prenatal, postnatal, and current nutritional status. Table 2 shows that birth weight and birth
height values were signicantly lower in males from the Low SA Group than in males from e High SA Group
(p = 0.0135 and p = 0.0175, respectively). Z-HC was lower for females from the Low SA Group than in the High
SA Group (p = 0.018). Although in the High SA Group and the Low SA Group, the means of BMI in males cor-
responded to the current nutritional status of healthy weight, and in females to overweight, BMI values did not
show signicant dierences between the groups.
Brain structural parameters volumes. We performed two analyses as follows. First, a whole-cortical
analysis of cortical thinness was carried out using a general linear model with PSU outcomes (language + math-
ematics), sex, and SES as regressors. en, a cortical and subcortical segmentation was carried out, including
independent regions of interest (ROI) of cortical areas derived from the rst analysis (see Methods). us, using
cluster-level correction, the cortical thinness analyses showed that the right inferior frontal gyrus thickness
(rIFG) positively correlated with PSU outcomes (CTD < 0.001, cluster corrected p < 0.05). Figure 2 shows the
T-value of the correlation between cortical thickness and PSU outcomes (corrected by sex and SES). Figure3
illustrates the p-value of this correlation (corrected by sex and SES) for clusters that survived the multiple com-
parison correction.
Next, cortical and subcortical brain segmentation was used to acquire relevant brain structural parameters.
Volumes expressed as mean ± SD by sex and group are shown in Table3. Males from the High SA Group had total
gray matter (p = 0.0027), le cerebellum cortex (p = 0.0008), brainstem (p = 0.0023), le hippocampus (p = 0.0033),
right cerebellum cortex (p = 0.0010), and right pallidum (p = 0.0051) volumes signicantly higher than their peers
Table 2. Prenatal, postnatal, and current nutritional status by group and sex. Results are expressed as
mean ± SD. Means were compared by Bonferroni’s test. SA: scholastic achievement; PSU: University Selection
Test; High SA Group: High PSU scores (> 620, > p75); Low SA Group: Low PSU scores (< 450, < p25). Z-HC,
head circumference-for-age Z-score; BMI: body mass index. p < .05 *; < .01**; < .001 ***
Nutritional indicators
Males Females
High SA Group
(n = 42) Low SA Group
(n = 16) p-value High SA Group
(n = 21) Low SA Group
(n = 23) p-value
Prenatal nutritional background
Birth weight (g) 3559 ± 591 2987 ± 717 .0135* 3236 ± 441 3399 ± 557 .3577
Birth height (cm) 50.8 ± 2.8 48.3 ± 3.1 .0175* 49.8 ± 2.8 49.6 ± 1.8 .8470
Postnatal nutritional background
Z-HC 0.80 ± 1.03 0.44 ± 1.13 .2491 0.16 ± 1.03 - 0.63 ± 1.09 .0182*
Current nutritional status
BMI 23.4 ± 3.2 25.3 ± 5.3 .0991 24.0 ± 3.5 25.7 ± 5.1 .2206
Content courtesy of Springer Nature, terms of use apply. Rights reserved
6
Vol:.(1234567890)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
from the Low SA Group, and the le accumbens area volume was signicantly lower (p = 0.0098). Females from
the High SA Group had brain segmentation without ventricles (p = 0.0017), total cortical gray matter (p = 0.0029),
le hemisphere cortical gray matter (p = 0.0038), right hemisphere cortical gray matter (p = 0.0022), total gray
matter (p = 0.0003), supratentorial (p = 0.0063), le cerebellum cortex (p = 0.0065), brain-stem (p = 0.0009), r ight
cerebellum WM (p = 0.0084) and cortex (p = 0.0009) volumes signicantly greater than their peers from the Low
SA Group.
Finally, Pearson correlations between the brain structural volume parameters and the PSU outcomes were
carried out by pooling High SA and Low SA groups by sex (see Methods and Table4). High correlations were
observed, especially in females, for brain segmentation without ventricles volume and outcomes in mathematics,
GMV with language and mathematics PSU outcomes, and brain-stem and right cerebellum cortex with PSU
mathematics outcome. Figure4 shows the correlation analysis between subcortical volume and the PSU outcomes
(language + mathematics) for both sexes.
Pearson correlation coecients matrix between PSU scores and most signicant param‑
eters. Pearson’s canonical and partial correlations were conducted to assess which signicant areas in the
initial ndings better explain the SA variance (see Table5). For the rIFG, the main result of the whole-cortical
analysis, the volume was extracted using an independent ROI (see Methods section). Regarding partial correla-
tions (Table5B), two independent correlations were analyzed: the rst includes total PSU scores, and the second
includes language and mathematics scores separately. In these analyses, positive and signicant correlations
were observed between IA and PSU scores for language (p < 0.0001) and mathematics (p < 0.0001). e PSU
outcomes positively and signicantly correlated with IA (p < 0.0001), GMV (p = 0.0022) and rIFG (p = 0.0140).
Language scores were positively and signicantly correlated with GMV (p = 0.0430), and mathematics scores
with IA (p < 0.0001) and rIFG (p = 0.0110). In addition, total GMV was positively and signicantly correlated
with Z-HC (p < 0.0001) and negatively correlated with BMI (p < 0.0001).
Multiple regression analysis between PSU outcomes (dependent variable) and most relevant
parameters (independent variables). e multiple regression analysis revealed that, independently of
sex, IA, GMV, rIFG, and SES were the independent variables more signicantly associated with PSU outcomes
Figure2. Cortical thickness results and their correlation with the University Selection Test outcomes (PSU)
(language + mathematics), corrected by sex and socioeconomic status. Colors represent the T-Value per vertex.
A: anterior; P: posterior, R: right, L: le.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
7
Vol.:(0123456789)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
(R2 = 0.811, Table6). e same was observed for the mathematics score (R2 = 0.750) and the language score (R2
= 0.770), except for the rIFG for the latter, which was not signicant.
Discussion
e present results support the study hypothesis, revealing that independently of sex, IA, GMV, rIFG, and SES
were the variables more signicantly associated with PSU outcomes. e total variability observed in the PSU
scores is explained as 81.1% (R2 = 0.811) by the eect of these variables. e results also reveal that the total
GMV and thickness of the rIFG explain the SA variance independently of the IA. ese ndings were observed
in both the language and mathematics scores, except for the rIFG in the language outcomes.
Several ndings have displayed that IA is the most stable and powerful predictor of SA in standardized
tests1,2,13,14,17–19,56–64. e mean correlation between general intelligence and academic performance is approxi-
mately 0.50, but it varies considerably depending on the variability of the measures and samples14,65–67. In our
study, the correlation between IA and PSU scores was 0.67, which agrees with our previous ndings in high
school graduates19. Interestingly, our ndings indicated that brain measures correlated with SA independently
of IA, suggesting that this marker could be more specically associated with SA.
e multiple regression analysis of brain structural parameters associated with PSU outcomes showed that
GMV and the rIFG were the most relevant brain parameters. In the present study, a high correlation was found
between GMV and Z-HC, a physical marker of past nutrition and brain development and an important anthro-
pometric indicator associated with SA and IA consistently reported in the literature24,27–30,68,69. Even though the
males of the High SA Group exhibited higher values of Z-HC than the rest of the sample, the results presented
here suggest that Z-HC may be a signicant indicator of IA or SA only for females. Findings by several authors
have shown that total brain volume is a good predictor of IA, specially GMV is associated with higher IA70,71.
ese ndings have been interpreted as the general intelligence depends on distributed areas throughout the
brain72–74.
Despite the plentiful research investigating the relationship between brain structure and intelligence, few
studies have focused on the relationship between the brain and SA. Prior work shows that prefrontal GM density
correlated with SA, and this correlation is partially mediated by general intelligence75. Moreover, the association
Figure3. Signicant clusters of the correlation between cortical thickness and the University Selection Test
outcomes (PSU) (language + mathematics), corrected by sex and socioeconomic status. Color represents the
p-value for a cluster in the right frontal gyrus that survives the multiple comparison correction (with the most
demanding correction, CTD p < 0.001, cluster corrected p < 0.05). A: anterior; P: posterior, R: right, L: le.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
8
Vol:.(1234567890)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
between frontal GM and SA persisted even aer adjusting for family SES and IA75. e prefrontal cortex is com-
monly highlighted as the center of individual dierences in general intelligence76,77. Baseline measurements of
frontal GMV predict verbal episodic memory performance changes over ten years of follow-ups78. In the context
of the results presented here, it is possible to postulate that frontal GM volumes could be a neuroanatomical
marker for SA partially independent of IA.
Particularly for prefrontal regions, the current results show that the rIFG contributes to explaining PSU
outcomes, but only in mathematics, which was unexpected. However, this result is in line with recent studies
investigating this issue by measuring neural activity associated with numerical magnitude processing acuity,
domain-general attention, and selective attention to numbers via functional MRI. Results showed that activation
in the IFG predicted achievements in mathematics79,80. In children and adolescents, the resting-state analysis
Table 3. Brain structural parameters volumes by sex and group. Results are expressed as mean ± SD. Means
were compared by Bonferroni’s test. SA: scholastic achievement; PSU: University Selection Test; High SA
Group: High PSU scores (> 620, > p75); Low SA Group: Low PSU scores (< 450, < p25). Bonferroni corrected
p < .05*; < .01**; < .001***
Brain structural parameters
volumes
Males Females
High SA Group
(n = 42) Low SA Group
(n = 16) p-value High SA Group
(n = 21) Low SA Group
(n = 23) p-value
volumes (cc)
Brain segmentation without ven-
tricles 1279.36 ± 82.80 1229.29 ± 114.67 .0705 1165.78 ± 93.18 1083.98 ± 68.16 .0017**
Cortical gray matter 547.76 ± 41.52 525.91 ± 40.56 .0768 500.09 ± 37.74 465.76 ± 34.19 .0029**
Le hemisphere cortical gray matter 274.23 ± 20.95 261.82 ± 20.46 .0473* 249.45 ± 19.56 232.39 ± 17.43 .0038**
Right hemisphere cortical gray matter 273.54 ± 20.73 264.08 ± 20.28 .1242 250.64 ± 18.31 233.38 ± 16.88 .0022**
Cerebral white matter 496.90 ± 39.76 493.80 ± 64.88 .8257 453.56 ± 42.91 427.64 ± 39.09 .0421*
Le hemisphere cerebral white matter 248.80 ± 19.89 246.38 ± 32.45 .7309 226.85 ± 21.85 213.14 ± 19.39 .0330*
Right hemisphere cerebral white
matter 248.10 ± 19.97 247.42 ± 32.55 .9236 226.71 ± 21.14 214.50 ± 19.80 .0545
Gray matter 751.55 ± 48.08 705.31 ± 55.26 .0027** 681.11 ± 52.12 628.74 ± 34.93 .0003***
Subcortical gray matter 66.38 ± 4.49 62.90 ± 5.76 .0181* 60.99 ± 4.89 57.68 ± 4.38 .0224*
Supratentorial 1130.84 ± 79.52 1101.80 ± 101.68 .2554 1032.82 ± 81.59 967.03 ± 70.09 .0063**
Le cerebellum white matter 15.86 ± 2.22 15.67 ± 2.28 .7690 16.08 ± 2.81 14.42 ± 1.77 .0235*
Le inferior frontal gyrus 2.71 ± 0.14 2.65 ± 0.16 .2202 2.76 ± 0.18 2.63 ± 0.12 .0172*
Le cerebellum cortex 65.54 ± 8.71 56.82 ± 7.34 .0008*** 57.87 ± 8.31 51.76 ± 5.71 .0065**
Le thalamus proper 8.32 ± 0.69 8.02 ± 0.83 .1729 7.85 ± 0.94 7.30 ± 0.61 .0252*
Le caudate 3.99 ± 0.47 3.82 ± 0.49 .2233 3.84 ± 0.46 3.55 ± 0.41 .0363*
Le putamen 6.02 ± 0.61 5.77 ± 0.81 .2155 5.63 ± 0.69 5.50 ± 0.77 .5610
Le pallidum 2.21 ± 0.24 1.95 ± 0.45 .0081** 1.86 ± 0.34 1.68 ± 0.34 .0919
Brainstem 24.01 ± 3.01 21.19 ± 2.98 .0023** 21.65 ± 2.83 19.01 ± 1.79 .0006***
Le hippocampus 4.69 ± 0.38 4.31 ± 0.48 .0033** 4.33 ± 0.46 4.09 ± 0.45 .0823
Le amygdala 1.82 ± 0.15 1.85 ± 0.17 .5701 1.58 ± 0.15 1.57 ± 0.16 .8600
Le accumbens area 0.54 ± 0.12 0.64 ± 0.12 .0098** 0.56 ± 0.15 0.56 ± 0.11 .8324
Le ventral dorsal caudate 4,61 ± 0.41 4.40 ± 0.46 .0947 4.12 ± 0.36 3.86 ± 0.35 .0186*
Right inferior frontal gyrus 2.82 ± 0.20 2.66 ± 0.12 .0039** 2.84 ± 0.23 2.65 ± 0.18 .0036**
Right cerebellum white matter 15.26 ± 2.17 15.08 ± 2.26 .7798 15.59 ± 2.41 13.83 ± 1.79 .0084**
Right cerebellum cortex 67.20 ± 8.53 58.79 ± 7.50 .0010** 59.70 ± 7.83 52.29 ± 5.84 .0009***
Right thalamus proper 7.97 ± 0.64 7.62 ± 0.73 .0806 7.29 ± 0.69 6.87 ± 0.57 .0368*
Right caudate 4.04 ± 0.45 3.85 ± 0.48 .1535 3.91 ± 0.48 3.59 ± 0.40 .0227*
Right putamen 5.99 ± 0.58 5.77 ± 0.72 .2506 5.52 ± 0.52 5.37 ± 0.59 .3662
Right pallidum 2.16 ± 0.23 1.86 ± 0.36 .0003*** 1.84 ± 0.35 1.70 ± 0.25 .1220
Right hippocampus 5.99 ± 0.58 4.60 ± 0.47 .0380* 4.50 ± 0.53 4.30 ± 0.43 .1632
Right amygdala 2.02 ± 0.19 1.96 ± 0.17 .2280 1.75 ± 0.18 1.63 ± 0.16 .0234*
Right accumbens area 0.70 ± 0.10 0.65 ± 0.09 .0732 0.64 ± 0.10 0.58 ± 0.07 .0259*
Right ventral dorsal caudate 4.59 ± 0.37 4.32 ± 0.43 .0213* 4.09 ± 0.36 3.83 ± 0.31 .0147*
Posterior corpus callosum 0.96 ± 0.15 1.02 ± 0.21 .2379 1.05 ± 0.13 0.96 ± 0.13 .0347*
Middle-posterior corpus callosum 0.49 ± 0.11 0.57 ± 0.12 .0192* 0.51 ± 0.12 0.52 ± 0.15 .8776
Central corpus callosum 0.53 ± 0.15 0.63 ± 0.17 .0414* 0.56 ± 0.14 0.52 ± 0.12 .2800
Middle-anterior corpus callosum 0.58 ± 0.18 0.63 ± 0.20 .3889 0.61 ± 0.16 0.58 ± 0.14 .5299
Anterior corpus callosum 0.90 ± 0.18 0.97 ± 0.19 .1957 0.97 ± 0.16 0.93 ± 0.17 .4793
Content courtesy of Springer Nature, terms of use apply. Rights reserved
9
Vol.:(0123456789)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
also reveals the association between IFG connectivity with intelligence81. Interestingly, the cerebellum cortex
and the brainstem present a high correlation with PSU scores, although these parameters were not selected by
the linear regression model. e cerebellum has been related to high cognitive function82,83 and likely presents
an important role in SA that must be studied in further research. Similarly, several key neuromodulator systems
that inuence cognitive performance, such as locus coeruleus, are settled into the brainstem84,85. Studies with a
greater spatial resolution are required to better identify the inuence of these systems on SA.
Notable, the described brain-SA association was carried out within the framework of a multidimensional
approach considering socioeconomic, intellectual, nutritional, and demographic variables. is approach is not
only to control for these variables but also to understand SA as a complex social and biological phenomenon.
Consequently, SA is associated with SES, maternal schooling, intelligence, and antecedents of malnutrition in the
rst year of life70,86,87. Accordingly, SES in our study was also signicantly correlated with PSU outcomes, likely
because poverty conditions are also associated with structural dierences in several areas of the brain86. Other
ndings revealed that childhood SES predicts executive function performance and measures of prefrontal corti-
cal function, specically in the association between family income and parental education and GM thickness86.
Despite socioeconomic indicators, such as parental schooling, occupation of the household head, and
housing characteristics, which were positive and signicantly associated with PSU scores in both language
Table 4. Pearson correlation coecients between brain structural parameters volumes and the University
Selection Test outcomes by sex. PSU: University Selection Test; FDR q < .05 *; q < .01**; q < .001 ***.
Brain structural parameters volumes
PSU (L + M) L anguage (L) Mathematics (M)
Males Females Males Females Males Females
Brain segmentation without ventricles .302 .506** .353* .458* .247 .512***
Cortical gray matter .326* .465* .364* .443* .275 .449*
Le hemisphere cortical gray matter .341* .456* .373* .442* .297* .433**
Right hemisphere cortical gray matter .308* .471* .352* .442* .250 .462**
Cerebral white matter .088 .345* .149 .301 .035 .361*
Le hemisphere cerebral white matter .096 .361* .153 .318* .047 .373*
Right hemisphere cerebral white matter .080 .328* .144 .282 .022 .347*
Gray matter .445* .574*** .477* .533*** .400* .568***
Subcortical gray matter .324* .362* .378** .317* .266* .378*
Supratentorial .227 .435* .285 .401* .166 .433**
Le inferior frontal gyrus .177 .356* .122 .383* .153 .303
Le cerebellum white matter .053 .323* .119 .257 −.004 .361*
Le cerebellum cortex .395* .487* .387* .429* .396* .504**
Le thalamus proper .224 .343* .248 .315* .193 .343*
Le caudate .175 .350* .230 .338* .129 .333*
Le putamen .190 .041 .264* −.007 .094 .083
Le pallidum .261 .357* .257 .355* .275 .331*
Brainstem .357* .575*** .383* .478* .321* .623***
Le hippocampus .433* .299 .461* .244 .375* .329*
Le amygdala −.037 −.001 −.039 −.038 −.033 .035
Le accumbens area −.242 −.160 −.201 −.183 −.280 −.124
Le ventral dorsal caudate .221 .382* .276* .320* .180 .410**
Right inferior frontal gyrus .346* .540*** .330* .443** .345** .590****
Right cerebellum white matter .045 .372* .116 .302* −.013 .409**
Right cerebellum cortex .390* .565*** .386* .496** .386* .585***
Right thalamus proper .226 .349* .268 .347* .192 .324*
Right caudate .206 .363* .258 .345* .162 .353*
Right putamen .158 .083 .218 .041 .010 .116
Right pallidum .414* .354* .415* .340* .405* .340*
Right hippocampus .320* .221 .366* .151 .256 .271
Right amygdala .178 .309* .201 .304* .161 .290
Right accumbens area .302 .370* .360* .306* .214 .402*
Right ventral dorsal caudate .303* .431* .348* .393* .270 .434**
Posterior corpus callosum −.227 .318* −.208 .346* −.227 .265
Middle-posterior corpus callosum −.327* −.082 −.306* −.088 −.308* −.069
Central corpus callosum −.242 .116 −.229 .124 −.238 .099
Middle-anterior corpus callosum −.140 .045 −.142 −.013 −.113 .096
Anterior corpus callosum −.239 .067 −.228 .089 −.226 .040
Content courtesy of Springer Nature, terms of use apply. Rights reserved
10
Vol:.(1234567890)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
and mathematics, SES was the only socioeconomic variable most signicantly associated with PSU outcomes
in the statistical regression model. Note that SES is a global construct that includes, among other indicators,
parental education. In this context, several studies have emphasized that parental education is another relevant
factor inuencing brain development and SA. Parental IA (especially maternal IA) is consistent in explaining
children’s IA, probably, because mothers are the primary source of intellectual stimulation and enrichment in
Figure4. Le: Individual example of subcortical segmentation. Colors represent the dierent structures used
in the correlation analysis (Brainstem and cerebellum, Caudate, Amygdala, Putamen, Hippocampus, alamus,
and Pallidum). Right: Correlation between the volume of each structure and the University Selection Test (PSU)
outcomes for both males and females. Color represents the correlation coecient.
Table 5. Pearson correlation coecients (A) and partial correlation coecients (B) matrices between the
University Selection Tests scores and most signicant parameters. PSU: University Selection Test; LPSU,
language University Selection Test score; MPSU, mathematics University Selection Test score; IA, intellectual
ability; GMV, gray matter volume; BW, birth weight; BL, birth length; Z-HC, head circumference-for-age
Z-score; BMI, body mass index; rIFG, right inferior frontal gyrus thickness. * p < .05; ** p < .01; *** p < .001;
**** p < .0001.
PSU LPSU MPSU IA GMV rIFG BW BL Z-HC BMI
(A)
LPSU .959**** –
MPSU .969**** .860**** –
IA .805**** .758**** .794**** –
GMV .575**** .563**** .548**** .460**** –
rIFG .442**** .395**** .456**** .392**** .264** –
BW .139 .139 .127 .108 .228* .029 –
BL .252* .249* .233* .182 .154 .007 .654**** –
Z-HC .393**** .407**** .335*** .294** .717**** .107 .316** .258* –
BMI −.261** −.217* −.293** −.275** −.114 .113 .224* −.016 .333*** –
SES .561**** .567**** .516**** .440**** .361**** .187 .056 .142 .317** −.094
LPSU MPSU PSU IA GMV BW BL Z-HC BMI SES
(B)
MPSU .23**
IA .420*** .470*** .670***
GMV .210* .160 .310** −.090
BW −.110 −.080 −.090 .100 .130
BL .130 .040 .170 −.120 −.150 .560***
Z-HC .005 −.020 −.030 .080 .670*** .070 .050
BMI .100 .030 .060 −.230* −.390*** .190 −.160 .510***
SES .140 .140 .280** .020 .007 .060 .040 .060 .010
rIFG .070 .260* .240* −.040 .120 −.190 .120 −.080 .110 .030
Content courtesy of Springer Nature, terms of use apply. Rights reserved
11
Vol.:(0123456789)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
the psycho-social environment and the health-related behavior of the family88–91. Parental education predicted
cortical thickness in the right anterior cingulate gyrus and the IFG, providing a meaningful link between SES
and cognitive function among healthy children92.
Our study has several limitations that must be considered when interpreting the presented results. First, the
use of multiple brain parameters increases the type I error, although standard corrections for multiple compari-
sons were performed. Second, many variables such as breastfeeding, birth weight according to gestational age,
Z-HC at birth, parental intelligence, and maternal stimulation at an early age could not be considered in the
present analysis. Many of these variables were not registered in the hospital records, and the mothers did not
remember them. Nor was it possible to measure the degree of parental stimulation because of the sample’s age.
Table 6. Multiple regression analysis between the University Selection Test (PSU) (mean
language + mathematics), language PSU and mathematics PSU scores (dependent variables) and the most
relevant parameters (independent variables). Model R2 = .811; Root MSE (Root mean squared error, standard
deviation of the dependent variable PSU) = 71.4949; Model F Value = 55.76, p < .0001. Model R2 = .770; Root
MSE (Root mean squared error, standard deviation of the dependent variable language PSU) = 75.9123; Model
F Value = 43.61, p < .0001. Model R2 = .750; Root MSE (Root mean squared error, standard deviation of the
dependent variable mathematics PSU) = 90.9008; Model F Value = 39.52, p < .0001. IA, intellectual ability;
IA grades: Grade I, superior; Grade II, above average; Grade III, average; Grade IV, below average; Grade V,
intellectually defective. GMV, gray matter volume. rIFG, right inferior frontal gyrus thickness. SES, socio-
economic status. e initial regressors (independent variables considered in the statistical model) considered
for the forward stepwise selection method were IA, SES, sex, brain segmentation without ventricles, GMV,
brainstem, rIFG, right cerebellum cortex, le cerebellum cortex.
Parameter Estimate Standard Error
of Estimate T for H0:
Parameter = 0 p >|T|
PSU score (mean language + mathematics)
Intercept −291.5 139.4 −2.09 .0393
IA (Ref: Grades IV + V)
Grade I + II 239.3 26.9 8.87 .0001
Grade III 49.8 26.0 1.92 .0586
GMV 0.59 0.15 3.90 .0002
rIFG 106.9 37.4 2.86 .0053
SES (Ref: Medium)
High SES 16.6 20.1 0.83 .4097
Low SES 44.6 18.4 2.41 .0178
Sex (Ref: males)
Females −27.8 19.1 1.45 .1505
Language PSU score
Intercept −251.3 148.0 −1.70 .0930
IA (Ref: Grades IV + V)
Grade I + II 235.9 28.6 8.24 .0001
Grade III 68.7 27.6 2.49 .0146
GMV 0.64 0.16 4.03 .0001
SES (Ref: Medium)
High SES 26.9 21.3 1.26 .2113
Low SES −41.5 19.6 −2.12 .0368
rIFG 71.0 39.7 1.79 .0770
Sex (Ref: males)
Females −35.8 20.3 1.76 .0817
Mathematics PSU score
Intercept −321.8 175.5 −1.83 .0699
IA (Ref: Grades IV + V)
Grade I + II 238.8 32.8 7.27 .0001
Grade III 27.5 32.0 0.86 .3912
rIFG 142.9 47.5 3.00 .0034
GMV 0.52 0.19 2.75 .0071
SES (Ref: Medium)
High SES 6.08 25.5 0.24 .8127
Low SES −49.1 23.2 −2.12 .0370
Sex (Ref: males)
Females −18.3 24.1 0.76 .4494
Content courtesy of Springer Nature, terms of use apply. Rights reserved
12
Vol:.(1234567890)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
Many parents were separated, unlocatable, or had died, so it was impossible to measure their intelligence with any
reliability. In this regard, it has been found that breastfed children had signicantly higher IA scores and larger
brain volume, GMV, total cortical GM, and subcortical GMV compared with non-breastfed children93. Early
postnatal nutrition is essential for brain growth and maturation, and WM connectivity strength may be a valuable
predictor of long-term cognitive functioning32,33,94. In addition, it has been found that low-risk preterm children
achieve lower scores in neurophysiological tests than children born at term, impacting brain volumes and cogni-
tive outcomes in the long term95–98, although our study did not consider that variable. Another relevant issue is
the possible dierences in the incidence of developmental disorders between the two studied groups. Although
participants have no history of or current developmental diagnosis, it is impossible to rule out undiagnosed
conditions. e participants of this study were a group of high school graduates with a narrow age range. Future
research should consider a wide range of factors, including elementary and high school students. Considering
that SA consists of dierent complex abilities, future studies should focus on exploring the associations between
SA and brain networks using task-based functional MRI. erefore, more research is needed to elucidate and
understand these mechanisms further.
Altogether, our ndings present evidence that GMV and the rIFG serve as the neural basis of academic
performance and reveal the role of general intelligence and SES in the association between brain structure and
SA. Knowing the neuronal subtract of SA can improve a not well-known eld of knowledge, shedding light on
the possible cognitive mechanisms. us, the results are relevant in explaining the complex interactions among
variables that aect PSU outcomes and can be helpful in the design and implementation of health and educational
policies to improve scholar performance. PSU outcomes are crucial for students to pursue successful collegiate
careers and to guide their future lives and prospects as adults by developing their talents and learning specic
skills for desired careers. In this context, evidence-based public policies and interventions may help the most
disadvantaged children through comprehensive health care, maternal education, and in-school care, enabling
them to develop their talents and achieve their promises and goals.
Data availability
e datasets generated are available upon request from the corresponding author.
Received: 29 November 2021; Accepted: 22 November 2022
References
1. Ivanovic, D. M. et al. A multifactorial approach of nutritional, intellectual, brain development, cardiovascular risk, socio-economic,
demographic and educational variables aecting the scholastic achievement in Chilean students: An eight- year follow-up study.
PLoS ONE 14, e0212279 (2019).
2. Ivanovic, D. M. et al. Impact of anthropometric nutritional parameters on the university selection test in Chile: A multifactorial
approach. Nutrit. Burbank Los Angeles Cty Calif. 57, 74–83 (2018).
3. Jaksić, I. Eight years of their lives: rough schooling to the labour market in Chile. Comp. Educ. Rev. 28, 145–146 (1984).
4. Colom, R. Chapter11 Intellectual abilities. Handb. Clin. Neurol. 173, 109–120 (2020).
5. Ivanovic, D. M. et al. Nutritional status, brain development and scholastic achievement of Chilean high-school graduates from
high and low intellectual quotient and socio-economic status. Brit. J. Nutr. 87, 81–92 (2002).
6. Lynn, R., Meisenberg, G., Mikk, J. & Williams, A. National IQs predict dierences in scholastic achievement in 67 countries. J.
Biosoc. Sci. 39, 861–874 (2007).
7. Colom, R. & Flores-Mendoza, C. E. Intelligence predicts scholastic achievement irrespective of SES factors: Evidence from Brazil.
Intelligence 35, 243–251 (2007).
8. Pangelinan, M. M. et al. Beyond age and gender: Relationships between cortical and subcortical brain volume and cognitive-motor
abilities in school-age children. Neuroimage 54, 3093–3100 (2010).
9. Schnack, H. G. et al. Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cereb.
Cortex 25, 1608–1617 (2015).
10. Gignac, G., Vernon, P. A. & Wickett, J. C. e scientic study of general intelligence. Part Ii Biol. G 1, 93–106. https:// doi. org/ 10.
1016/ b978- 00804 3793-4/ 50042-8 (2003).
11. Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M. & Voracek, M. Meta-analysis of associations between human brain volume and
intelligence dierences: How strong are they and what do they mean?. Neurosci. Biobehav. Rev. 57, 411–432 (2015).
12. Nave, G., Jung, W. H., Linnér, R. K., Kable, J. W. & Koellinger, P. D. Are bigger brains smarter? Evidence from a large-scale prereg-
istered study. Psychol. Sci. 30, 43–54 (2018).
13. Ivanovic, R. M., Forno, H. S., Castro, C. G. & Ivanovic, D. M. Intellectual ability and nutritional status assessed through anthro-
pometric measurements of Chilean school-age children from dierent socioeconomic status. Ecol. Food Nutr. 39, 35–59 (2000).
14. Ivanovic, D. M. et al. Brain development parameters and intelligence in Chilean high school graduates. Intelligence 32, 461–479
(2004).
15. Nestor, P. G. et al. Dissociating prefrontal circuitry in intelligence and memory: Neuropsychological correlates of magnetic reso-
nance and diusion tensor imaging. Brain Imaging Behav. 9, 839–847 (2014).
16. Torre, G.-A.A. & Eden, G. F. Relationships between gray matter volume and reading ability in typically developing children,
adolescents, and young adults. Dev. Cogn. Neuros-neth 36, 100636 (2019).
17. Ivanovic, D. M. et al. Impact of nutritional status at the onset of elementary school on academic aptitude test achievement at the
end of high school in a multicausal approach. Br. J. Nutrit. 102, 142–154 (2009).
18. Ivanovic, D. M. et al. Scholastic achievement: A multivariate analysis of nutritional, intellectual, socioeconomic, sociocultural,
familial, and demographic variables in Chilean school-age children. Nutrition 20, 878–889 (2004).
19. Ivanovic, D. M. et al. Head size and intelligence, learning, nutritional status and brain development. Neuropsychologia 42, 1118–
1131 (2004).
20. Ivanovic, D. M. et al. Brain development and scholastic achievement in the Education Quality Measurement System tests in Chilean
school-aged children. Pediatr Res 75, 464–470 (2014).
21. Meruelo, A. D. et al. Early adolescent brain markers of late adolescent academic functioning. Brain Imaging Behav. 13, 945–952
(2019).
22. Chaddock-Heyman, L. et al. Scholastic performance and functional connectivity of brain networks in children. PLoS ONE 13,
e0190073 (2018).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
13
Vol.:(0123456789)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
23. Tyborowska, A. et al. Early-life and pubertal stress dierentially modulate grey matter development in human adolescents. Sci.
Rep.-UK 8, 9201 (2018).
24. Winick, M. & Rosso, P. e eect of severe early malnutrition on cellular growth of human brain. Pediatr. Res. 3, 181–184 (1969).
25. Winick, M. & Rosso, P. Head circumference and cellular growth of the brain in normal and marasmic children. J. Pedi atr. 74,
774–778 (1969).
26. Ivanovic, D. M. Does undernutrition during infancy inhibit brain growth and subsequent intellectual development?. Nutrition 12,
568–571 (1996).
27. Ivanovic, D. M., Olivares, M. G., Castro, C. G. & Ivanovic, R. M. Nutrition and learning in Chilean school age children: Chile’s
Metropolitan Region survey 1986–1987. Nutrition 12, 321–328 (1996).
28. Stoch, M. B. & Smythe, P. M. Does undernutrition during infancy inhibit brain growth and subsequent intellectual development?.
Arch. Dis Child 38, 546–552 (1963).
29. Stoch, M. B. & Smythe, P. M. e eect of undernutrition during infancy on subsequent brain growth and intellectual development.
South Afr. Med. J Suid-afrikaanse Tydskrif Vir Geneeskunde 41, 1027–1030 (1967).
30. Stoch, M. B. & Smythe, P. M. 15-Year developmental study on eects of severe undernutrition during infancy on subsequent
physical growth and intellectual functioning. Arch. Dis Child 51, 327 (1976).
31. Brown, J. L. & Pollitt, E. Malnutrition, poverty and intellectual development. Sci. Am. 274, 38–43 (1996).
32. Keunen, K. et al. White matter maturation in the neonatal brain is predictive of school age cognitive capacities in children born
very preterm. Dev. Med. Child Neurol. 59, 939–946 (2017).
33. Keunen, K., van Elburg, R. M., van Bel, F. & Benders, M. J. N. L. Impact of nutrition on brain development and its neuroprotective
implications following preterm birth. Pediatr. Res. 77, 148–155 (2014).
34. Coviello, C. et al. Eects of early nutrition and growth on brain volumes, white matter microstructure, and neurodevelopmental
outcome in preterm newborns. Pediatr. Res. 83, 102–110 (2018).
35. Valenzuela, R . et al. Docosahexaenoic acid levels in erythrocytes and their association with the University Selection Test Outcomes
in Chile. Prostaglandins Leukot Essent Fat Acids 139, 25–30 (2018).
36. Zamroziewicz, M. K., Paul, E. J., Zwilling, C. E. & Barbey, A. K. Determinants of uid intelligence in healthy aging: Omega-3
polyunsaturated fatty acid status and frontoparietal cortex structure. Nutr. Neurosci. 21, 570–579 (2017).
37. Gur, R. E. et al. Burden of environmental adversity associated with psychopathology, maturation, and brain behavior parameters
in youths. JAMA Psychiat. 76, 966 (2019).
38. Tooley, U. A., Bassett, D. S. & Mackey, A. P. Environmental inuences on the pace of brain development. Nat. Rev. Neurosci. 22,
372–384 (2021).
39. Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773–778
(2015).
40. Cavanagh, J. et al. Socioeconomic status and the cerebellar grey matter volume. Data from a well-characterised population sample.
Cerebellum 12, 882–891 (2013).
41. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. Neuroimage 9, 179–194 (1999).
42. Sharp, D. J., Scott, S. K. & Wise, R. J. S. Monitoring and the controlled processing of meaning: Distinct prefrontal systems. Cereb
Cortex 14, 1–10 (2004).
43. Iglesias, J. E. et al. A computational atlas of the hippocampal formation using exvivo, ultra-high resolution MRI: Application to
adaptive segmentation of invivo MRI. Neuroimage 115, 117–137 (2015).
44. Alvarez, M. L., Muzzo, S. & Ivanović, D. Scale for measurement of socioeconomic level, in the health area. Rev. Med. Chil. 113,
243–249 (1985).
45. Raven, J. Handbook of Nonverbal Assessment. 223–237 (2003). https:// doi. org/ 10. 1007/ 978-1- 4615- 0153-4_ 11.
46. Garcia-Garzon, A. & Garrido, L. E. Searching for G: A new evaluation of SPM-LS dimensionality. J. Intell. 7, 14 (2019).
47. de Onis, M. Development of a WHO growth reference for school-aged children and adolescents. B World Health Organ. 85, 660–667
(2007).
48. Ivanovic, D., Olivares, M., Castro, C. & Ivanovic, R. Head circumference in Chilean school children 5 to 18 years of age and
socioeconomic status. Rev. Med. Chil. 123, 587–599 (1995).
49. Nellhaus, G. Head circumference from birth to eighteen years. Practical composite international and interracial graphs. Pediatrics
41, 106–114 (1968).
50. Roche, A. F., Mukherjee, D., Guo, S. M. & Moore, W. M. Head circumference reference data: Birth to 18 years. Pediatrics 79,
706–712 (1987).
51. Hager, R., Lu, L., Rosen, G. D. & Williams, R. W. Genetic architecture supports mosaic brain evolution and independent brain–body
size regulation. Nat Commun 3, 1079 (2012).
52. Zhen, Z., Tian, J., Qin, W. & Zhang, H. Partial correlation mapping of brain functional connectivity with resting state fMRI. P Soc.
Photo-opt. Ins. 651112–651112–9 (2007). https:// doi. org/ 10. 1117/ 12. 709012.
53. Joshi, A. A. et al. Anatomical structural network analysis of human brain using partial correlations of gray matter volumes. 2010
IEEE Int Symposium Biomed Imaging Nano Macro 844–847 (2010). https:// doi. org/ 10. 1109/ isbi. 2010. 54901 18.
54. Worsley, K. J., Taylor, J. E., Tomaiuolo, F. & Lerch, J. Unied univariate and multivariate random eld theory. Neuroimage 23,
S189–S195 (2004).
55. Fan, L. et al. e human brainnetome atlas: A new brain atlas based on connectional architecture. Cereb Cortex 26, 3508–3526
(2016).
56. Ivanovic, D. M. et al. Long-term eects of severe undernutrition during the rst year of life on brain development and learning in
Chilean high-school graduates. Nutrition 16, 1056–1063 (2000).
57. Ivanovic, D. et al. Twelve-year follow-up study of the impact of nutritional status at the onset of elementary school on later edu-
cational situation of Chilean school-age children. Eur. J. Clin. Nutr. 62, 18–31 (2007).
58. Kuncel, N. R., Hezlett, S. A. & Ones, D. S. Academic performance, career potential, creativity, and job performance: Can one
construct predict them all?. J. Pers. Soc. Psychol. 86, 148–161 (2004).
59. Rostain, A. L. Assessing and managing adolescents with school problems. Adolesc. Med. Phila Pa 8, 57–76 (1997).
60. Sternberg, R. J., Grigorenko, E. & Bundy, D. A. e predictive value of IQ. Merrill-palmer Q 47, 1–41 (2001).
61. Watkins, M. W., Lei, P.-W. & Canivez, G. L. Psychometric intelligence and achievement: A cross-lagged panel analysis. Intelligence
35, 59–68 (2007).
62. Deary, I. J., Strand, S., Smith, P. & Fernandes, C. Intelligence and educational achievement. Intelligence 35, 13–21 (2007).
63. Freberg, M. E., Vandiver, B. J., Watkins, M. W. & Canivez, G. L. Signicant factor score variability and the validity of the WISC-III
full scale IQ in predicting later academic achievement. Appl. Neuropsychol. 15, 131–139 (2008).
64. Gil-Espinosa, F. J., Chillón, P. & Cadenas-Sanchez, C. General intelligence was associated with academic achievement but not
tness in adolescents aer one year. Acta Paediatr. Oslo Nor 1992(108), 896–902 (2018).
65. Neisser, U. et al. Intelligence: Knowns and unknowns. Am. Psychol. 51, 77–101 (1996).
66. Suleman, Q. et al. Association between emotional intelligence and academic success among undergraduates: A cross-sectional
study in KUST, Pakistan. PLoS ONE 14, e0219468 (2019).
67. Zaboski, B. A., Kranzler, J. H. & Gage, N. A. Meta-analysis of the relationship between academic achievement and broad abilities
of the Cattell-horn-Carroll theory. J. School Psychol. 71, 42–56 (2018).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
14
Vol:.(1234567890)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
68. Stoch, M. B., Smythe, P. M., Moodie, A. D. & Bradshaw, D. Psychosocial outcome and CT ndings aer gross undernourishment
during infancy: A 20-year developmental study. Dev. Med. Child Neurol. 24, 419–436 (2008).
69. Plaza, B. L. et al. e impact of malnutrition on brain development, intelligence and school work performance. Arch. Latinoam.
Nutr. 51, 64–71 (2001).
70. Haier, R. J., Jung, R. E., Yeo, R. A., Head, K. & Alkire, M. T. Structural brain variation and general intelligence. Neuroimage 23,
425–433 (2004).
71. Cox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M. & Deary, I. J. Structural brain imaging correlates of general intel-
ligence in UK Biobank. Intelligence 76, 101376 (2019).
72. Colom, R., Jung, R. E. & Haier, R. J. Distributed brain sites for the g-factor of intelligence. Neuroimage 31, 1359–1365 (2006).
73. Menary, K. et al. Associations between cortical thickness and general intelligence in children, adolescents and young adults. Intel-
ligence 41, 597–606 (2013).
74. Hilger, K., Ekman, M., Fiebach, C. J. & Basten, U. Intelligence is associated with the modular structure of intrinsic brain networks.
Sci. Rep.-UK 7, 16088 (2017).
75. Wang, S. et al. Examining gray matter structure associated with academic performance in a large sample of Chinese high school
students. Sci. Rep.-UK 7, 893 (2017).
76. Duncan, J. Frontal lobe function and general intelligence: Why it matters. Cortex 41, 215–217 (2005).
77. Roca, M. et al. Executive function and uid intelligence aer frontal lobe lesions. Brain J. Neurol. 133, 234–247 (2009).
78. Aljondi, R., Szoeke, C., Steward, C., Yates, P. & Desmond, P. A decade of changes in brain volume and cognition. Brain Imaging
Behav. 13, 554–563 (2018).
79. Bellon, E., Fias, W., Ansari, D. & Smedt, B. D. e neural basis of metacognitive monitoring during arithmetic in the developing
brain. Hum. Brain Mapp. 41, 4562–4573 (2020).
80. Wilkey, E. D. & Price, G. R. Attention to number: e convergence of numerical magnitude processing, attention, and mathematics
in the inferior frontal gyrus. Hum. Brain Mapp. 40, 928–943 (2018).
81. Li, C. & Tian, L. Association between resting-state coactivation in the parieto-frontal network and intelligence during late child-
hood and adolescence. AJNR Am. J. Neuroradiol. 35, 1150–1156 (2014).
82. Jacobi, H., Faber, J., Timmann, D. & Klockgether, T. Update cerebellum and cognition. J. Neurol. 268, 3921–3925 (2021).
83. Buckner, R. L. e cerebellum and cognitive function: 25 Years of insight from anatomy and neuroimaging. Neuron 80, 807–815
(2013).
84. Dutt, S., Li, Y., Mather, M., Nation, D. A. & Initiative, A. D. N. Brainstem substructures and cognition in prodromal Alzheimer’s
disease. Brain Imag. Behav. 15, 2572–2582 (2021).
85. Grueschow, M., Kleim, B. & Ru, C. C. Role of the locus coeruleus arousal system in cognitive control. J. Neuroendocrinol. 32,
e12890 (2020).
86. Hair, N. L., Hanson, J. L., Wolfe, B. L. & Pollak, S. D. Association of child poverty, brain development, and academic achievement.
JAMA Pediatr. 169, 822–829 (2015).
87. Voss, W., Jungmann, T., Wachtendorf, M. & Neubauer, A. P. Long-term cognitive outcomes of extremely low-birth-weight infants:
the inuence of the maternal educational background. Acta Paediatr Oslo Nor 1992(101), 569–573 (2012).
88. Lean, R. E., Paul, R. A., Smyser, C. D. & Rogers, C. E. Maternal intelligence quotient (IQ) predicts IQ and language in very preterm
children at age 5 years. J. Child Psychol. Psychiatry All. Discipl. 59, 150–159 (2017).
89. Ronfani, L. et al. e complex interaction between home environment, socioeconomic status, maternal IQ and early child neuro-
cognitive development: A multivariate analysis of data collected in a newborn cohort study. PLoS ONE 10, e0127052 (2015).
90. Torres, D. D. Understanding how family socioeconomic status mediates the maternal intelligence-child cognitive outcomes rela-
tionship: A moderated mediation analysis. Biodemogr. Soc. Biol. 59, 157–177 (2013).
91. Whitley, E. et al. Inuence of maternal and paternal IQ on ospring health and health behaviours: evidence for some trans-
generational associations using the 1958 British birth cohort study. Eur. Psychiatry J. Assoc. Eur. Psychiatrists 28, 219–224 (2012).
92. L awson, G. M., Duda, J. T., Avants, B. B., Wu, J. & Farah, M. J. Associations between children’s socioeconomic status and prefrontal
cortical thickness. Dev. Sci 16, 641–652 (2013).
93. Luby, J. L., Belden, A. C., Whalen, D., Harms, M. P. & Barch, D. M. Breastfeeding and childhood IQ: e mediating role of gray
matter volume. J. Am. Acad Child Adolesc. Psychiatry 55, 367–375 (2016).
94. Allotey, J. et al. Cognitive, motor, behavioural and academic performances of children born preterm: A meta-analysis and systematic
review involving 64 061 children. BJOG Int. J. Obstetr. Gynaecol. 125, 16–25 (2017).
95. Arhan, E. et al. Regional brain volume reduction and cognitive outcomes in preterm children at low risk at 9 years of age. Child’s
Nerv. Syst. CHNS 33, 1317–1326 (2017).
96. Srzentić, M. K., Raguž, M. & Ozretić, D. Specic cognitive decits in preschool age correlated with qualitative and quantitative
MRI parameters in prematurely born children. Pediatr. Neonatol. 61, 160–167 (2020).
97. Vollmer, B. & Edmonds, C. J. School age neurological and cognitive outcomes of fetal growth retardation or small for gestational
age birth weight. Front. Endocrinol. 10, 186 (2019).
98. Young, J. M. et al. Deep grey matter growth predicts neurodevelopmental outcomes in very preterm children. Neuroimage 111,
360–368 (2015).
Acknowledgements
is work was supported by Grants 1100431 and 1150524 from the National Fund for Scientic and Techno-
logical Development (FONDECYT) (PI: Daniza Ivanovic). e authors are grateful to the Agency for Quality
Education, the Studies Center of the Ministry of Education of Chile, and the Department of Evaluation, Measure-
ment, and Educational Registry (DEMRE) of the University of Chile for the facilities to carry out this research,
which reviewed, approved, and authorized this study, and for providing PSU outcomes. e authors also thank
Dr. Oscar Brunser, MD, and Anne Bliss, Ph.D., for their helpful comments and suggestions.
Author contributions
D.I., P.B., and F.Z. developed the conceptualization of the study; D.I., T.R., A.A., C.Bu., F.V., R.V., and C.Ba.
participated in data collection; C.L. and C.S. performed the analysis for brain development study MRI exams;
P.B. and F.Z. performed the analysis for brain volumetric parameters; D.I. planned the application of statistical,
mathematical and computational techniques to analyze or synthesize study data, and prepared gures; D.I. and
V.A. applied and analyzed IA tests; D.I. was the principal investigator for funding acquisition; D.I., P.B., F.Z. and
P.S-I. wrote the original dra. All authors reviewed and approved the nal version to be published and agreed
to be accountable for the integrity and accuracy of all aspects of the work.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
15
Vol.:(0123456789)
Scientic Reports | (2022) 12:20562 | https://doi.org/10.1038/s41598-022-24958-0
www.nature.com/scientificreports/
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to D.I.orP.B.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Open Access is article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons licence, and indicate if changes were made. e images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
© e Author(s) 2022
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.
