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Objective: The Computerized Neurocognitive Battery (CNB) enables efficient neurocognitive assessment. The authors aimed to (a) estimate validity and reliability of the battery's Dutch translation, (b) investigate effects of age across cognitive domains, and (c) estimate heritability of the CNB tests. Method: A population-representative sample of 1,140 participants (aged 10-86), mainly twin-families, was tested on the CNB, providing measures of speed and accuracy in 14 cognitive domains. In a subsample (246 subjects aged 14-22), IQ data (Wechsler Intelligence Scale for Adults; WAIS) were available. Validity and reliability were assessed by Cronbach's alpha, comparisons of scores between Dutch and U.S. samples, and investigation of how a CNB-based common factor compared to a WAIS-based general factor of intelligence (g). Linear and nonlinear age dependencies covering the life span were modeled through regression. Heritability was estimated from twin data and from entire pedigree data. Results: Internal consistency of all tests was moderate to high (median = 0.86). Effects of gender, age, and education on cognitive performance closely resembled U.S. Samples: The CNB-based common factor was completely captured by the WAIS-based g. Some domains, like nonverbal reasoning accuracy, peaked in young adulthood and showed steady decline. Other domains, like language reasoning accuracy, peaked in middle adulthood and were spared decline. CNB-test heritabilities were moderate (median h2 = 31%). Heritability of the CNB common factor was 70%, similar to the WAIS-based g-factor. Conclusion: The CNB can be used to assess specific neurocognitive performance, as well as to obtain a reliable proxy of general intelligence. Effects of aging and heritability differed across cognitive domains. (PsycINFO Database Record
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The Computerized Neurocognitive Battery: Validation, Aging Effects, and
Heritability Across Cognitive Domains
Suzanne C. Swagerman, Eco J. C. de Geus,
Kees-Jan Kan, Elsje van Bergen,
and Harold A. Nieuwboer
VU University Amsterdam
Marinka M. G. Koenis and Hilleke E. Hulshoff Pol
University Medical Center Utrecht
Raquel E. Gur and Ruben C. Gur
University of Pennsylvania
Dorret I. Boomsma
VU University Amsterdam
Objective: The Computerized Neurocognitive Battery (CNB) enables efficient neurocognitive assessment.
The authors aimed to (a) estimate validity and reliability of the battery’s Dutch translation, (b) investigate
effects of age across cognitive domains, and (c) estimate heritability of the CNB tests. Method: A population-
representative sample of 1,140 participants (aged 10 86), mainly twin-families, was tested on the CNB,
providing measures of speed and accuracy in 14 cognitive domains. In a subsample (246 subjects aged
14 –22), IQ data (Wechsler Intelligence Scale for Adults; WAIS) were available. Validity and reliability were
assessed by Cronbach’s alpha, comparisons of scores between Dutch and U.S. samples, and investigation of
how a CNB-based common factor compared to a WAIS-based general factor of intelligence (g). Linear and
nonlinear age dependencies covering the life span were modeled through regression. Heritability was
estimated from twin data and from entire pedigree data. Results: Internal consistency of all tests was moderate
to high (median 0.86). Effects of gender, age, and education on cognitive performance closely resembled
U.S. samples. The CNB-based common factor was completely captured by the WAIS-based g. Some
domains, like nonverbal reasoning accuracy, peaked in young adulthood and showed steady decline. Other
domains, like language reasoning accuracy, peaked in middle adulthood and were spared decline. CNB-test
heritabilities were moderate (median h2 31%). Heritability of the CNB common factor was 70%, similar
to the WAIS-based g-factor. Conclusion: The CNB can be used to assess specific neurocognitive perfor-
mance, as well as to obtain a reliable proxy of general intelligence. Effects of aging and heritability differed
across cognitive domains.
Keywords: neurocognition, intelligence, heritability, aging, computerized testing
Supplemental materials:
Cognitive performance varies greatly among individuals. Possible
sources of individual variation are gender, age, and genetic and
environmental factors. Studies on cognitive functioning increasingly
aim to find the biological basis of cognition in brain substrates or
genetic variants. These neurobiological and genetic association stud-
ies on individual differences in cognition require reliable and well-
defined phenotypes obtained in large numbers of participants. Such
studies would benefit greatly from the availability of cognitive tests
that are optimally suited to explore mechanistic neurobiological and
neurodevelopmental models in large samples. Understanding how
cognitive functions develop across the life span and how they are
influenced by environmental and genetic factors is critical for eluci-
dating healthy and pathological brain function.
As cognitive functions may be differentially sensitive to sources of
variation, both basic functions, such as processing speed or attention,
and more complex functions, like reasoning or emotion processing,
require consideration. Notably, neurocognitive tests based on func-
tional neuroimaging are designed to activate specific brain systems,
whereas traditional neuropsychological and intelligence tests may
activate multiple brain systems simultaneously, making the latter less
suitable in neurobiological studies (Gur, Erwin, & Gur, 1992).
To address the need for an efficient and comprehensive neuro-
cognitive battery, the Brain Behavior Laboratory of the University
of Pennsylvania has developed the Web based Computerized Neu-
Suzanne C. Swagerman, Department of Biological Psychology, VU
University Amsterdam; Eco J. C. de Geus, Department of Biological
Psychology, VU University Amsterdam and EMGO
Institute of Health
and Care Research, VU University Medical Center; Kees-Jan Kan, Elsje
van Bergen, and Harold A. Nieuwboer, Department of Biological Psychol-
ogy, VU University Amsterdam; Marinka M. G. Koenis and Hilleke E.
Hulshoff Pol, Brain Center Rudolf Magnus, Department of Psychiatry,
University Medical Center Utrecht; Raquel E. Gur and Ruben C. Gur,
Brain Behavior Laboratory, Department of Psychiatry, Perelman School of
Medicine, University of Pennsylvania; Dorret I. Boomsma, Department of
Biological Psychology, VU University Amsterdam.
Correspondence concerning this article should be addressed to Suzanne
C. Swagerman, Department of Biological Psychology, VU University
Amsterdam, van der Boechorststraat 1, 1081 BT Amsterdam, the Nether-
lands. E-mail:
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Neuropsychology © 2016 American Psychological Association
2016, Vol. 30, No. 1, 53–64 0894-4105/16/$12.00
rocognitive Battery (CNB; Gur et al., 2001;Gur et al., 2010;Gur
et al., 2012). This battery is the result of an iterative validation
process during which tests and test items were selected. Tests aim
to target specific brain regions, which was validated in functional
brain imaging studies (Roalf et al., 2014). Since its introduction,
the CNB has undergone minor revisions including shortening of
tests and adding new ones. The current version of the CNB (Gur
et al., 2012) yields quantitative performance (accuracy and speed)
measures in five neurobehavioral functions: executive-control,
memory, complex cognition, social cognition, and sensorimotor
speed. More specifically, within these five neurobehavioral func-
tions the battery assesses performance across 14 cognitive do-
mains, which are described in Table 1 and described in Supple-
mentary Material S1.
The need for an efficient and reliable neurocognitive battery
extends beyond the English speaking countries for large-scale
genetic, developmental and aging studies. For this reason we
translated test instructions and test items from English into Dutch.
International collaborative studies would benefit from the assur-
ance that cognitive batteries can be deployed universally: cognitive
performance and effects such as sex and age should be comparable
across countries.
The objectives of this article are first to estimate validity and
reliability of the battery’s Dutch translation, second to investigate
effects of age across cognitive domains, and third to estimate how
these cognitive abilities are influenced by environmental and ge-
netic factors. With regard to the validation part of our study, we
aim to confirm reliability, validity, and feasibility in home and
laboratory settings of the CNB in a large population-based sample
of 1140 participants (10 86 years). Here we present indices of
reliability based on internal consistency (Cronbach’s alpha) and on
intercorrelations among the test scores. To confirm validity, we
compare mean scores and effects of gender and age in the Dutch
to the U.S. population. In addition, we correlate CNB scores to
measures of a person’s own and parental level of education. We
also consider whether the CNB can provide scores comparable to
intelligence scores as derived from traditional intelligence tests. If
so, this would provide further convergent validity, because, al-
though individual CNB test scores will be difficult to compare to
traditional IQ scores, across batteries the sources of between test
covariance can be expected to be the same (Johnson, te Nijenhuis,
& Bouchard, 2008), genetic sources in particular (Plomin & Ko-
vas, 2005).
Table 1
Cognitive Domains and Test Names, Order of Administration, and Mean Administration Time (in Minutes), Number of Participants
Who Completed the Test, and the Test‘s Mean Score (and SD), Cronbach’s Alpha Coefficients () of Accuracy Score (Percentage or
Number of Correct Responses) and Speed (Median Response Time, in ms)
Accuracy Speed
Cognitive domain Test name
label Order Duration NMSD
Executive control
Abstraction/flexibility Penn Conditional Exclusion Test
CET 9 4.9 1,125 1.9 .8
2813.3 1392.6
Attention Penn Continuous Performance
CPT 3 5.3 1,125 54.8 5.4 .86 487.7 49.1 .82
Working memory Letter-N-Back Test
LNB 6 9.2 1,114 18.8 1.8 .77 537.7 118.0 .80
Verbal memory Penn Word Memory Test
Immediate CPW-i 5 3.1 1,125 36.3 2.8 .62 1564.5 368.2 .92
Delayed CPW-d 8 1.1 1,124 35.0 3.3 .64 1541.7 376.6 .91
Face memory Penn Facial Memory Test
Immediate CPF-i 4 3.9 1,123 31.4 3.5 .56 1992.7 544.2 .92
Delayed CPF-d 7 1.5 1,121 32.1 3.5 .57 1834.2 489.7 .89
Spatial memory Visual Object Learning Test
Immediate VOLT-i 13 2.7 1,117 16.0 2.3 .48 1973.8 554.6 .87
Delayed VOLT-d 17 .5 1,115 15.4 2.4 .48 1811.5 519.7 .86
Complex cognition
Nonverbal reasoning Penn Matrix Reasoning Test MAT 12 7.8 1,129 13.9 5.2 .90 10806.0 6959.8 .88
Language reasoning Penn Verbal Reasoning Test
VRT 14 1.8 1,123 69.2 20.6 .53 8465.8 3332.5 .74
Spatial ability Variable Penn Line Orientation
LOT 16 5.5 1,119 12.9 3.7 .79 10506.8 3861.8 .97
Social cognition
Emotion identification Penn Emotion Identification Test EI 2 2.3 1,132 32.1 3.5 .62 2273.4 685.7 .92
Emotion differentiation Measured Emotion Differentiation
EDT 10 3.4 1,131 28.0 3.5 .69 3721.0 1369.1 .94
Age differentiation Age Differentiation Test ADT 15 3.0 1,122 26.8 3.9 .74 3238.4 1493.5 .94
Sensorimotor speed Motor Praxis Test MP 1 1.8 1,130 20.0 .4 .93 793.2 221.3 .95
Motor speed Penn Computerized Finger-Tapping
TAP 11 3.5
ccc c
110.6 15.1 .96
Note.Nnumber of participants; M mean score; SD standard deviation.
Short test version.
Different items for children.
No accuracy score available for TAP.
Not amenable for calculating.
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Once we have established that the CNB provides reliable and
valid measures of cognition, we can explore the etiology of indi-
vidual differences in these cognitive phenotypes. These extend
beyond sex- and linear age effects: therefore our second aim is to
estimate nonlinear effects of age across the life span. Many cog-
nitive functions improve as children mature, but with different
trajectories for different functions: a well-known example is the
late development of executive functions compared to memory
(Gur et al., 2012). However, later in life cognitive abilities start to
decrease again, especially in the domains of processing speed,
memory, and executive functioning, although there is currently
little agreement on the time of onset of this decline (Salthouse,
2009;Schaie, 2005;Deary et al., 2009). Cognitive aging is most
often studied in a small age range (i.e., only elderly), usually
including only one or a few cognitive functions. Here we will
explore the patterns of development across cognitive domains and
covering the life span.
Our third and final aim regards environmental and genetic
effects on the cognitive tests. Initial studies on a subset of the tests
show heritability estimates between 10 and 70% (Calkins et al.,
2010;Greenwood et al., 2007;Gur et al., 2007) in the U.S.
population. These estimates are based on selected samples of
schizophrenia patients. We will extend these findings by estimat-
ing heritability for all accuracy and speed scores in an unselected
sample, which facilitates generalization to the general population.
We will also estimate heritability of the common variance among
the CNB test scores. Because indicators of common variance
among psychometric IQ tests (i.e., general factors of intelligence)
are the most heritable among the indicators of intelligence, with an
estimated heritability coefficient of 50% to 80% (Jensen, 1998;
Plomin, 2012), we expect a high heritability. If so in our analyses,
this would further confirm validity.
Heritability was estimated using two approaches, both based on
the resemblance in cognitive performance among family members
as a function of their genetic relatedness. Half of our sample
consisted of twins; the other half of parents, siblings, and children
of twins and siblings. The first approach is based on information
from the mono- and dizygotic twin pairs, who are of the same age
by definition, and estimate the extent to which their resemblance is
due to shared genes, or common environmental influences shared
by offspring growing up in the same family. In the second ap-
proach we extend the analyses to data from the entire pedigree, that
is, all family members, where cross-generation resemblance is
analyzed simultaneously with the resemblance in twin pairs. These
pedigree-based analyses provide information on genetic stability
across generations.
Participants were mainly recruited by the Netherlands Twin
Register (NTR), which is a population-based register that recruits
twins and other multiples, their parents, siblings, spouses, and
offspring (Boomsma et al., 2006;Willemsen et al., 2013;van
Beijsterveldt et al., 2013). In total there were 1,140 participants,
mainly (n1,110) from 431 families who were recruited from all
regions in the Netherlands. The other 30 subjects were university
students. Most participants (621) were part of a twin pair or triplet.
Twin pairs were monozygotic (54 male, 100 female pairs) or
dizygotic (42 male, 60 female, 71 opposite sex pairs). The rest of
the sample consisted of siblings (150), parents of twins (279),
partners of twins and siblings (51), and offspring of twins and
siblings (9). The age range was from 10 to 86 (M37.73, SD
20.86). The figure in Supplementary Material S2 depicts the age
distribution of these 472 males (41.4%) and 668 females. On
average, participants had 12.92 years of education (SD 3.29).
The average number of years of education in their parents was
12.34 (similar to the average in the Dutch population, UNESCO
Institute for Statistics, 2013).
Studies and procedures were approved by the Medical Ethics
Review Committee of the VU Medical Center Amsterdam and the
Central Committee on Research Involving Human Subjects. Par-
ticipants were approached by mail. When they (and possibly other
family members) were willing to participate, a structured tele-
phone call followed. This phone call had the purpose of informing
participants and of asking about exclusion criteria. Exclusion cri-
teria were epilepsy or paralysis, and physical problems that would
influence test performance (like a broken arm or severe vision
Testing took place at the VU Laboratory (n358), at the
participants’ home (n536), or in the laboratory of the University
Medical Center Utrecht (n246). In all settings, test conditions
were controlled to prevent disturbance or distractions. Prior to the
start of the testing, the administrator fully explained the procedure,
after which written informed consent was obtained. Participants of
12 years of age and older signed themselves. For children up to 16
years parents needed to sign as well. Following the CNB protocol
from the Brain Behavior Laboratory, participants were asked to
complete a reading test (Swagerman et al., in press). For none of
the participants did the reading test indicate that they were unable
to complete the CNB. Participants received a gift voucher and
compensation for their traveling costs. All participants received
feedback on their performance, in the form of a graph in which
their score was ranked with participants of the same age.
Standardized procedures were followed for both the home and
laboratory test location. The participant sat at a desk, with the test
administrator behind him or her. Macbooks were used for admin-
istration with identical mouse and screen settings. All participants
were instructed to use only the mouse and spacebar for responses
(laptop mousepad was disabled).
Prior to the start of each test, the administrator read the test
instructions out loud to the participant, after which the participant
was provided with practice trials (except for the memory tests and
the Conditional Exclusion Test). The practice trials had to be
completed successfully in order to start the test. During the cog-
nitive assessment, the experimenters kept track of whether test
scores were valid, based on the participant’s apparent motivation
or interruption of the test session. Automated test score validation
occurred upon upload to the Pennsylvania web servers that host the
CNB (Gur et al., 2012). Completion of the battery lasted on
average 1.5 hr (ranging between approximately 50 min and 3 hr),
including optional breaks at three designated points.
A subsample of adolescent participants (n246, 14 –22 years
old), took part in the Brainscale study on development of brain and
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cognition (van Soelen et al., 2012). These participants completed
a shortened version of the Wechsler Intelligence Scale for Adults
(WAIS; Wechsler, 1997) on the same day as they were assessed on
the CNB.
In addition to the CNB, participants were asked about, or filled
out a questionnaire on lifestyle (drinking, smoking, exercise be-
havior), height, weight, and medication use.
Cognitive battery. The Dutch translation of the current CNB
includes a total of 17 tests, yielding measures of performance
(accuracy and speed) in 14 cognitive domains (Table 1; Supple-
mentary Material S1). All test instructions and test items were
translated from English into Dutch, and back-translated by a pro-
fessional translator. In addition, the frequency of the words in the
A and B versions of the Word Memory Test and Verbal Reasoning
Test were compared, to ensure that both versions were of equal
Accuracy was defined as the percentage or number of correct
responses on a test. Measures of speed were derived from the
median response time in milliseconds of correct responses, and
were multiplied by 1. Hence for both accuracy and speed, higher
scores denote better performance. The Finger tapping test (TAP)
did not provide accuracy scores: the score reflected the number of
taps one can produce within 10 seconds over 6 attempts. TAP
score thus constitutes a speed score, where a relatively high score
denotes relatively fast motor speed.
Psychometric IQ. The shortened WAIS included two verbal
and two performance tests, which were, in order of assessment,
Vocabulary (verbal), Block Design (nonverbal), Similarities (ver-
bal), and Matrix Reasoning (nonverbal). Using normative tables
per age group, raw test scores were transformed into standardized
scale scores (Wechsler, 1997). Then a correction for the number of
excluded subtests was applied (2 out of 6 verbal and 2 out of 5
nonverbal tests) to obtain total (TIQ), verbal (VIQ), and perfor-
mance IQ (PIQ).
Years of education. Participants were asked how many years of
education they and their parents had completed. Repeating a school
year did not count as an extra year. In case the same type of education
was repeated at a higher level (e.g., economics degree at college level
followed by university level), only the number of years at the highest
level was counted. Parental education was defined as the mean num-
ber of years of paternal and maternal education, or of one of them if
the other was unknown.
Statistical Analyses
Validity and reliability. Excluding test scores of children un-
der 13 (n4) and scores that were judged invalid (0.8%), we
calculated in SPSS 21.0 for each test the average accuracy score,
average speed score, average duration, and the Cronbachs’s alpha
coefficient of internal consistency (not possible for the Conditional
Exclusion Test). Further, correlations among accuracy scores, corre-
lations among speed scores, and per test the correlation between
accuracy score and speed score were calculated (all while correcting
for effects of gender and age). Accuracy and speed scores were
skewed. In addition, the data had to be considered as clustered since
the study involved family members. Statistical analyses (other than
the genetic analyses) thus required correction of the standard errors of
the parameters. This was accomplished by analyzing the data in the
statistical program R (version 3.1.1, R Core Team, 2014) using
packages lavaan (Rosseel, 2012) and lavaan.survey (Oberski, 2014),
by including family number as cluster variable (each student received
a unique family number), and by opting for robust sandwich estima-
tion. This procedure allowed for the analysis of clustered, non-
normally distributed, but continuous outcome variables.
Following Gur et al. (2010) we obtained gender differences on all
cognitive measures, and correlations between performance scores and
education as well as parental education. Because own educational
level is meaningful only after the typical age that maximal academic
training can be achieved, we restricted these analyses to a subsample
over age 30 (n632, M14.2, sd 3.4).
In the literature, the variance that is common to IQ subtest scores is
usually described by the latent variable referred to as general intelli-
gence or simply g(Jensen, 1998;Spearman, 1904). A strong corre-
lation between the common variance in CNB test performance and
general intelligence (as derived from traditional batteries) would im-
ply that once performance measures on the CNB are aggregated, a
CNB sum score would be similar to a traditional TIQ score. TIQ can
be considered to constitute the most accurate proxy of general intel-
ligence (after the g-factor score). The WAIS VIQ, PIQ, and TIQ scores
that were available in the subsample therefore provided the opportunity
to test this using the following approach.
We selected all CNB accuracy scores, those on the Motor Praxis
test excluded, because WAIS scores are based on accuracy scores
rather than speed scores, and concern cognitive abilities and knowl-
edge rather than motor skills. Next, we forced a confirmatory oblique
2-factor model on the (WAIS and CNB accuracy) data, in which the
CNB scores loaded on one latent factor (labeled g-CNB in Figure 1)
and the WAIS VIQ and PIQ scores on the other (labeled g-WAIS). As
WAIS IQ scores are already age corrected, we added linear and
(mean-centered) quadratic age terms as predictors of the CNB scores
to make them comparable to the WAIS. The correlation between the
two latent factors was considered to indicate the strength to which the
common variance among the CNB accuracy test scores relates to
general intelligence as assessed by the WAIS. A high correlation
would indicate that the CNB can provide a valid and reliable estima-
tion of general intelligence. To be able to confirm this, we obtained
factor scores on the g-CNB and correlated these with WAIS TIQ
scores. This correlation was interpreted as a measure of both reliabil-
ity and cross-validity.
Analyses of aging effects. Relations between test performance
scores and age were analyzed according to a model in which the
scores on a particular test were regressed on age (across the age range
in the data: 13– 86 years old) and on (mean centered) age squared.
Heritability analyses. To estimate heritability, data of monozy-
gotic (MZ) twins who are (nearly) genetically identical and dizygotic
(DZ) twin pairs who share on average half of their segregating genes
were analyzed first (Boomsma, Busjahn, & Peltonen, 2002). Because
MZ and DZ twins differ in their genetic similarity, genetic effects are
suggested for a trait if the MZ correlation is higher than the DZ
correlation. Effects of common environment shared by twins are
suggested to also contribute to twin resemblance when the DZ cor-
relation is larger than half the MZ correlation. Modeling of twin data
was performed in OpenMx (Boker et al., 2011) by raw-data maxi-
mum likelihood. All speed scores were log-transformed prior to
analysis to reduce skewness (to the right toward slow response times)
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
and heteroscedasticity (more variance with older age). First, in a
saturated model, means, variances, and twin correlations were esti-
mated for monozygotic (MZ) and dizygotic (DZ) twin pairs. Next,
parameters representing the influence of additive genetic factors (A),
common environment shared by twins (C) and unique environment
(E, including measurement error) were estimated (Plomin, Defries,
Knopik, & Neiderhiser, 2013). The model included gender, age, and
(mean centered) age
as moderators of the mean scores. Second,
heritability was estimated in Mendel (Lange, Westlake, & Spence,
1976;Lange et al., 2013), analyzing the entire pedigree structure
including twins. The approach implemented in Mendel takes the
entire pedigree information to estimate variance components and
allows for the inclusion of all relatives. The effect of common envi-
ronment (C) was estimated for twins and their nontwin siblings
growing up in the same household up to age 22 (mean age when
children move out of their parents’ house, Statistics Netherlands,
2014). Heritability analyses were performed for the 15 accuracy and
17 speed outcomes. As 98% of all participants had perfect accuracy
on the Motor praxis test, for the sensorimotor domain only speed was
examined. In addition, heritability was estimated for both the factor
score on the g-CNB and WAIS TIQ scores.
Internal consistencies and intercorrelations. Table 1 in-
cludes general information about the cognitive tests and domains,
mean duration, mean accuracy and speed score, and Cronbach’s
alpha coefficient. These coefficients of internal consistency were
high for speed (median 0.92) and moderate to high for accuracy
(median 0.62). Table 2 summarizes the intercorrelations among
the performance scores. When intercorrelations were estimated
without correcting for gender and age, results are similar but
generally a little stronger. As expected, correlations among accu-
racy scores were all positive (although the magnitudes ranged
considerably, mostly small to moderate). Intercorrelations among
speed scores were for the majority positive with magnitudes rang-
ing from small to large. Correlations between accuracy score and
speed of each test varied considerably, ranging from negative and
large (0.73, nonverbal reasoning) to positive and moderate (0.26,
verbal memory) with a median of 0.07.
Some tests were thus characterized by a tendency of better accu-
racy being accompanied by faster response time, whereas others were
characterized by a tradeoff, where better accuracy was accompanied
by slower response time (the nonverbal reasoning test in particular).
Gender differences. Figure 2 depicts the mean gender differ-
ences on the performance measures. We found that females tended
to score more accurate on all social cognition tests as well as the
face and word memory tests (negative effects in Table 3) whereas
males showed higher scores in the language reasoning, spatial
ability and spatial memory (delayed) tests (positive effects in
Table 3). Regarding speed, males were faster on the motor speed
and spatial ability tests, and females on the verbal memory (de-
layed), emotion identification, and age differentiation tests.
Education and parental education. Figure 3 provides the
correlations between cognitive performance and education. The
correlations between years of education and accuracy were all
positive, ranging from small (0.16, age differentiation) to moderate
(0.49, language reasoning). Those with speed ranged from mod-
erately negative 0.17 (nonverbal reasoning) to moderately pos-
Figure 1. Oblique two-factor model of overlap in variance of Computerized Neurocognitive Battery (CNB)
tests and Weschsler Intelligence Scale for Adults (WAIS) Verbal and Performance IQ scales. Circles represent
the two latent variables that describe common variance among CNB tests (labeled g-CNB) and common variance
among WAIS subtests (labeled g-WAIS). Squares represent the observed CNB test scores and WAIS Verbal and
Performance IQ scores. Double-headed arrows between two variables represent correlations and single-headed
arrows between two variables represent standardized regression effects (factor loadings included). Any other
single-headed arrows represent residuals.
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
itive 0.39 (verbal memory. Their medians were moderate and
positive (accuracy 0.29; speed 0.20).
Correlations between mean parental education and cognitive
accuracy were also positive and also ranged from small (0.05,
sensorimotor speed) to moderate (0.28 nonverbal reasoning). Cor-
relations with parental education and speed ranged from negative
and small (0.02, nonverbal reasoning) to positive and moderate
0.31 (sensorimotor speed). Both medians were positive but small
(accuracy 0.14; speed 0.04).
Relation to psychometric intelligence. The mean IQ scores
in the subsample of 246 participants who completed the shortened
WAIS were comparable to the population average of 100 (SD
15): VIQ 102.44 (SD 13.76), PIQ 106.15 (SD 14.25), TIQ
103.80 (SD 12.74). The tests that correlated highest with IQ
were Word Memory and Verbal- and Matrix Reasoning (see
Supplementary Material S3).
Fitting the oblique two-factor model (see Figure 1) showed that
the latent g-CNB factor and the common g-WAIS factor had to be
considered to represent the same construct, because the estimated
correlation between the two factors equaled 1.0, denoting a perfect
relation. That overall performance on the CNB compares well to
cognitive performance as assessed by the traditional WAIS IQ test
battery was confirmed by the high correlation between the factor
scores on the g-CNB and WAIS Total IQ, which was 0.82.
In conclusion, the results imply that, corrected for age effects,
overall performance on the CNB compares well to general intelli-
gence as assessed by a psychometric intelligence test battery. This
would suggest that one does not need an intelligence battery in
addition to the CNB in order to obtain estimates of general intelli-
gence (next to performance measures of specific neurocognitive func-
tioning). In the interest of possible future assessment of intelligence
via the CNB, Supplementary Material S3 includes a description of
how to calculate IQ scores based on CNB test scores.
Analyses of Aging Effects
The correlations between cognitive performance and age (see Sup-
plementary Figure S4 for illustration) ranged in magnitudes from
positively small (0.15, language reasoning) to negatively moderate
(0.35, emotion identification) for accuracy. Associations with speed
were all negative, and ranged from small (0.03, language reasoning)
to moderate (0.53, spatial memory delayed). The contributions of
linear and quadratic age effects are detailed in Table 3. Examples of
the curvilinear age dependencies are visualized in Figure 4 (see
Supplementary Figures S5 for all CNB tests).
In general, the results clearly indicate that test performance tends to
decline as a nonlinear function of age, but also that the pattern of
decline differs across the cognitive domains. Often, cognitive perfor-
mance peaked during childhood or adolescence after which perfor-
mance gradually declined with a steeper slope after this peak: this was
seen for many of the speed measures, and accuracy on nonverbal
reasoning, attention, and most memory tests. However, for other
domains, like language reasoning (accuracy), performance increased
into middle adulthood and was followed by limited decline.
Heritability Analyses
Overall, twin correlations (Supplementary Table S6) of mo-
nozygotic twin pairs were larger than of dizygotic twin pairs,
Table 2
Intercorrelations Between Accuracy (Upper Triangle) and Speed (Lower Triangle) and Cross Correlations Between Accuracy and Speed (on Diagonal, Underscored)
Executive control Memory Complex cognition Social cognition Sensorimotor
Cognitive domain (test name) CET CPT LNB CPW-i CPW-d CPF-i CPF-d VOLT-i VOLT-d MAT VRT LOT EI EDT ADT MP TAP
Abstraction/flexibility (CET) .12 .18 .12 .16 .15 .10 .08 .12 .13 .23 .17 .18 .18 .12 .10
Attention (CPT) .17 .21 .20 .16 .22 .21 .16 .11 .14 .27 .24 .21 .16 .19 .16
Working memory (LNB) .14 .50 .18 .16 .20 .15 .16 .10 .11 .25 .25 .12 .13 .16 .11
Verbal Memory—immediate (CPW-i) .24 .35 .20 .26 .58 .23 .21 .22 .18 .25 .22 .14 .15 .20 .14
Delayed (CPW-d) .25 .34 .20 .78 .16 .28 .26 .17 .20 .27 .25 .20 .12 .24 .12
Face Memory—immediate (CPF-i) .24 .25 .12 .52 .53 .14 .62 .21 .22 .22 .22 .18 .26 .20 .18
Delayed (CPF-d) .22 .27 .13 .52 .62 .74 .07 .20 .21 .23 .22 .19 .25 .21 .21
Spatial Memory—immediate (VOLT-i) .28 .21 .06 .51 .57 .55 .55 .03 .52 .26 .22 .16 .12 .15 .12
Delayed (VOLT-d) .21 .21 .08 .43 .53 .48 .54 .65 .06 .26 .21 .21 .10 .19 .15
Nonverbal reasoning (MAT) .25 .01 .06 .01 .05 .14 .16 .15 .19 .73 .42 .33 .20 .33 .24
Language reasoning (VRT) .24 .17 .15 .18 .21 .26 .22 .25 .27 .31 .03 .24 .19 .27 .19
Spatial ability (LOT) .27 .21 .11 .35 .38 .39 .41 .44 .45 .28 .33 .15 .12 .31 .27
Emotion Identification (EI) .22 .28 .18 .49 .43 .48 .47 .36 .28 .10 .24 .33 .14 .24 .22
Emotion Differentiation (EDT) .35 .23 .16 .36 .38 .48 .47 .44 .34 .28 .35 .45 .47 .07 .46 —
Age Differentiation (ADT) .27 .17 .11 .26 .32 .46 .48 .46 .38 .30 .29 .45 .36 .62 .03 ——
Sensorimotor speed (MP) .15 .27 .18 .50 .40 .25 .27 .24 .17 .01 .06 .24 .43 .21 .13
Motor speed (TAP) .07 .24 .14 .32 .28 .17 .18 .13 .14 .10 .01 .21 .18 .15 .06 .30
Note. Correlations are corrected for age and sex.
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suggesting effects of genetic influences on individual differences
in test performance.
Genetic modeling of twin data (see Table 4) showed moderate
heritability for the majority of the tests. For accuracy, heritability
ranged from 0 (ADT) to 52% (nonverbal reasoning, median of
31%). For speed measures, heritability ranged from 15 (working
memory) to 49% (face memory delayed, median of 33%). For
nearly all cognitive domains, influences of the common environ-
ment (C) were absent or small (between 0 and 24%), mostly seen
in the social cognition domain.
Heritability estimates based on all available pedigree informa-
tion were highly similar: between 13 and 49% of the total variance
in speed and accuracy could be attributed to genetic factors. These
results imply that expression of genes that influence cognitive
performance are stable over generations.
Individual differences in the factor scores on the latent variable
g-CNB were 70% heritable, without any evidence for C, whether
based on twin data or on all available family data. This was close
to the heritability of Total IQ on the WAIS: 75%.
The aim of this article was threefold: the first was to establish
reliability and validity of the Dutch translation of the Computer-
ized Neurocognitive Battery (CNB). The second was to explore
how cognitive domains, as measured by the CNB, develop across
the life span. The third was to estimate how these cognitive
abilities are influenced by environmental and genetic factors. We
conclude, based on a nonselected sample consisting of family
members, that the CNB is a reliable and valid instrument in the
Dutch population, with comparable scores to the U.S. studies. As
part of the validation objective in our analyses, we report high
Cronbach’s alpha’s across all tests. These indices of internal con-
sistency are slightly lower than those reported by Gur et al. (2010),
but this is likely due to the use of shortened tests. Intercorrelations
among cognitive tests were of small to moderate magnitude, but of
similar magnitude in the Netherlands and the United States without
correcting for effects of age and gender. The Dutch and U.S.
samples further show similar mean accuracy scores. The Dutch
sample demonstrated somewhat longer response times than the
U.S. sample, which probably reflects the fact that the age range of
the Dutch sample was broader and included more elderly (see also
Another part of the validation of the CNB concerned exploration
of the role of two well-known covariates of cognitive performance:
gender and age. Compared to the results from the U.S. sample, we
found effects that were overall similar, although small differences
can be noticed. For example, in the Dutch study males and females
performed about equally well on tests measuring attention and
Figure 2. Mean scores (and their 95% confidence intervals) of cognitive test scores in men (dark grey) and
women (light grey). See Table 1 for abbreviations of cognitive tests. No accuracy score available for the Finger
Tapping Test (TAP). CET Penn Conditional Exclusion Test; CPT Penn Continuous Performance Test;
LNB Letter-N-Back Test; MP Motor Praxis Test; TAP Penn Computerized Finger-Tapping Test;
CPW-i Penn Word Memory Test-Immediate; CPW-d Penn Word Memory Test-Delayed; CPF-i Penn
Facial Memory Test-Immediate; CPF-d Penn Facial Memory Test-Delayed; VOLT-i Visual Object
Learning Test-Immediate; VOLT-d Visual Object Learning Test-Delayed; MAT Penn Matrix Reasoning
Test; VRT Penn Verbal Reasoning Test; LOT Variable Penn Line Orientation Test; EI Penn Emotion
Identification Test; EDT Measured Emotion Differentiation Test; ADT Age Differentiation Test.
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working memory, whereas Gur et al. (2010,Figure 3) reported
lower attention scores for males and higher working memory for
females. However, generalizing across all CNB tests, standardized
effect sizes were distributed around zero, which suggests the
absence of an overall gender effect. This fits with findings from the
literature on intelligence: whenever gender differences are found
(also in the Dutch population, e.g., van der Sluis et al., 2008), they
are usually test specific and small, and the consensus is that there
is no evidence for any gender difference in overall cognitive
performance (Hyde, 2014).
Regarding age effects, the broader age range of the Dutch
sample is a likely explanation of the finding that correlations with
age tended to be stronger in this sample compared to the U.S.
sample (Gur et al., 2010). Yet, the overall picture was the same:
Older age is associated with slower as well as less accurate
performance, although across cognitive domains the associations
with age vary considerably in strength. CNB results are well in line
with previous findings from research into cognitive aging (Salt-
house, 2009). These findings have shown that the relation between
age and cognitive performance is quadratic: (Young) adults often
outperform children and adolescents as well as older adults and
elderly. Further, they indicate that the shape and rate of cognitive
decline tend to differ across domains, and cognitive decline is
particularly strong for measures of cognitive speed. In the current
sample, cognitive decline in accuracy performance was relatively
strong in the domain of attention and nonverbal reasoning. In
contrast, decline in verbal reasoning was relatively spared, as the
onset was late and the decline progressed at a fairly slow pace.
These observations also fit with the differences in growth curves
as derived from traditional psychometric tests. Crystalized cogni-
tive abilities (typically measured by verbal knowledge IQ tests)
continue to increase with age, whereas fluid abilities (typically
measured by nonverbal cognitive processing tests) show a peak in
adulthood followed by decline (Christensen, 2001;Baltes, 1987).
It should be noted that our analyses are cross-sectional. This has
the disadvantage that they cannot control for cohort effects like the
Flynn effect. On the other hand, cross-sectional studies have the
advantage that they are not influenced by retest-effects on test
scores (Salthouse, 2009;Hofer & Sliwinski, 2001).
Returning to the validation part of our study, convergent validity
was indicated by the association of individual test scores with
general indices of educational attainment (here operationalized as
years of own education and years of parental education), similar to
the U.S. population. Positive correlations between cognitive per-
formance and own and parental educational attainment were ap-
parent, although the strengths varied considerably across measure-
sand domains. This held for accuracy measures as well as speed
measures. This reiterates the general finding that cognitive perfor-
mance and educational attainment are associated (Deary & John-
son, 2010), but not equally strong for all measures (Ardila,
Ostrosky-Solis, Rosselli, & Gomez, 2000).
We further demonstrated convergent validity of the CNB by the
strong relation between the common variance across CNB tests
and general intelligence as assessed by the WAIS using a latent
factor approach. It should be noted, however, that overall scores on
the CNB can never fully predict the total IQ score of the WAIS
because observed scores will always be affected by measurement
error. Nevertheless the high correlation between the CNB factor
scores and WAIS TIQ (0.82) suggests that global measures of
Table 3
Standardized Effect Size (or Correlation) of Univariate Modeling of Effects of Age, Sex, Education (in Years, in Participants Over 30
Years of Age) and Mean Parental Education (in Years) on Accuracy and Speed
(in years)
(in years)
(females 0,
males 1)
(in years)
(in years)
Cognitive domain (test name) Accuracy Speed Accuracy Speed Accuracy Speed Accuracy Speed Accuracy Speed
Executive control
Abstraction/flexibility (Conditional Exclusion Test) .32
.04 .08 .29
Attention (Continuous Performance Test) .10
.01 .04 .39
Working memory (Letter-N-Back Test) .17
.02 .05 .02 .25
.08 .16
Verbal memory—immediate (Word Memory Test) .12
.06 .27
Verbal memory—delayed .21
Face memory—immediate (Facial Memory Test) .01 .12
.04 .02 .28
Face memory—delayed .13
.03 .32
Spatial memory—immediate (Object Learning Test) .24
.03 .03 .29
Spatial memory—delayed .21
.06 .13
.03 .25
Complex cognition
Nonverbal reasoning (Matrix Reasoning Test) .30
.11 .03 .07
Language reasoning (Verbal Reasoning Test) .15
.03 .29
.04 .09
.02 .49
.01 .12
Spatial ability (Line Orientation Test) .15
Social Cognition
Emotion identification (Emotion Identification Test) .35
Emotion differentiation (Emotion Differentiation Test) .15
.04 .29
Age differentiation (Age Differentiation Test) .11
Sensorimotor speed (Motor Praxis Test) .10
.03 .03 .19
.05 .31
Motor speed (Computerized Finger-Tapping Test) .21
Significant at ␣⫽.05.
Significant at ␣⫽.01.
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Figure 3. Correlations (and their 95% confidence intervals) of the cognitive tests with participants’ own level
of education and their parents’ level of education. Correlations with accuracy scores are given in black and with
speed scores in gray. See Table 1 for abbreviations of cognitive tests. No accuracy score available for the Finger
Tapping Test (TAP). CET Penn Conditional Exclusion Test; CPT Penn Continuous Performance Test;
LNB Letter-N-Back Test; MP Motor Praxis Test; TAP Penn Computerized Finger-Tapping Test;
CPW-i Penn Word Memory Test-Immediate; CPW-d Penn Word Memory Test-Delayed; CPF-i Penn
Facial Memory Test-Immediate; CPF-d Penn Facial Memory Test-Delayed; VOLT-i Visual Object
Learning Test-Immediate; VOLT-d Visual Object Learning Test-Delayed; MAT Penn Matrix Reasoning
Test; VRT Penn Verbal Reasoning Test; LOT Variable Penn Line Orientation Test; EI Penn Emotion
Identification Test; EDT Measured Emotion Differentiation Test; ADT Age Differentiation Test.
Figure 4. The curvilinear relation between cognitive test scores and age, including 95% confidence intervals.
Females are given in black (), males in gray (Œ). A: Language reasoning accuracy. B: Nonverbal reasoning
accuracy. C: Sensorimotor speed. Note that cognitive decline is more pronounced in B and C than A.
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CNB performance can be used as a good proxy of the universally
used total WAIS IQ.
The CNB is a valuable instrument not only for research, but also
for clinical purposes. Clinical neuropsychological examinations
regularly include intelligence and cognitive testing, because cog-
nitive dysfunction is often a characteristic of psychiatric disorders
(Millan et al., 2012). A well-known example is attention-deficit/
hyperactivity disorder, but impairments in attention, memory or
planning are also frequently seen in patients with schizophrenia or
mood- and anxiety disorders (Heinrichs & Zakzanis, 1998;Marvel
& Paradiso, 2004;Castaneda, Tuulio-Henriksson, Marttunen, Su-
visaari, & Lönnqvist, 2008). Traditional neuropsychological tests
are often designed to obtain a diagnosis on whether cognitive
functioning is abnormal. The CNB has a similar clinical utility,
because it provides quantitative measures of functioning, and
yields a patients’ profile of strengths and weaknesses. It may in
addition shorten the clinical cognitive assessment, as obtaining
global measures from the CNB makes the use of an additional
psychometric intelligence test unnecessary. This reduces adminis-
tration time as well as the burden for patients or participants.
Finally, the heritability analyses showed moderate estimates
with wide ranges for both accuracy (1–52%) and speed (14 –50%)
and are in line with the studies in the U.S. samples (Calkins et al.,
2010;Greenwood et al., 2007;Gur et al., 2007). In addition,
estimates based on twin data closely resembled those based on
family data, demonstrating that heritability estimates do not nec-
essarily have to be based on twin data, even though twins form a
perfectly controlled design because of equal environmental factors
like age and prenatal environment. Furthermore, family pedigree
analyses enable the study of cross-generation resemblance. From
Table 4
Variance Components Explained by Additive Genetic Effects (Heritability) Based on Twins, and Based on All Family Members,
Including 95% Confidence Intervals (CI)
Twins All family members
Cognitive domain (test name) Heritability CI Heritability CI
Executive control
Abstraction/flexibility (Conditional Exclusion Test) Accuracy 12 0–26 13 03–23
Speed 41 0–53 38 28–48
Attention (Continuous Performance Test) Accuracy 42 19–56 38 26–49
Speed 30 0–51 40 32–48
Working memory (Letter-N-Back Test) Accuracy 23 0–41 22 07–37
Speed 15 0–47 31 20–41
Face memory (Word Memory Test) Accuracy 31 0–49 34 22–46
Speed 43 21–56 36 25–47
Delayed Accuracy 35 0–48 31 22–41
Speed 49 17–60 43 32–54
Verbal memory (Facial Memory Test) Accuracy 27 1–40 26 13–39
Speed 41 12–53 44 34–53
Delayed Accuracy 16 0–32 18 03–33
Speed 36 0–49 36 26–45
Spatial memory (Object Learning Test) Accuracy 30 0–44 31 21–40
Speed 33 0–46 33 24–42
Delayed Accuracy 31 1–44 30 20–39
Speed 35 1–48 33 23–43
Complex cognition
Nonverbal reasoning (Matrix Reasoning Test) Accuracy 52 15–63 40 30–51
Speed 46 1–57 34 23–45
Language reasoning (Verbal Reasoning Test) Accuracy 29 0–42 37 26–48
Speed 31 0–49 31 22–39
Spatial ability (Line Orientation Test) Accuracy 46 25–57 49 42–56
Speed 34 0–51 30 20–41
Social cognition
Emotion identification (Emotion Identification Test) Accuracy 27 0–48 14 00–29
Speed 30 0–50 37 27–46
Emotion differentiation (Emotion Differentiation Test) Accuracy 4 0–34 17 05–30
Speed 23 0–50 35 25–45
Age differentiation (Age Differentiation Test) Accuracy 0 0–37 22 12–33
Speed 40 11–52 32 21–42
Sensorimotor speed (Motor Praxis Test) Speed 19 0–53 45 35–55
Motor speed (Computerized Finger-Tapping Test) Speed 38 14–51 31 19–43
General intelligence
g-CNB (measured by latent factor of CNB accuracy scores) 70 52–77 68 61–75
Total IQ (measured by WAIS) 75 61–84
Note. g-CNB general factor of intelligence, Computerized Neurocognitive Battery; CNB Computerized Neurocognitive Battery; WAIS Wechsler
Intelligence Scale for Adults.
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our analyses on cognitive performance, it can be concluded that
family members resemble each other mostly because of shared
genetic factors, and only to a small extent due to shared environ-
ment. The relatively large component of unshared environmental
factors is in agreement with other studies on specific neurocogni-
tive traits like attention or working memory (Polderman et al.,
2007;Kremen et al., 2007). Similar to heritability estimates of
general intelligence (Haworth et al., 2010), the variance common
to subtests showed a high heritability of 70%. This is higher than
the heritability coefficients of the variance in single CNB test
scores, which is in agreement with the common finding that
(intelligence) subtests demonstrate lower heritability coefficients
than factors of general intelligence (Kan, Wicherts, Dolan, & van
der Maas, 2013). Heritability of test scores (compared to g) may
first be reduced due to measurement error. Second, genetic effects
that influence specific cognitive performance tend to accumulate
as a function of the tests’ specificity, with aggregated measures
showing the highest heritability. As genetic effects on specific
cognitive abilities become blurred in general outcome measures
like g, we advise future studies to focus on the specific cognitive
functions, rather than general cognitive performance measures. In
sum, our findings are in line with results from both research into
specific neurocognitive functioning and general intelligence, pro-
viding vast evidence for the validity of the CNB.
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Received April 30, 2015
Revision received August 31, 2015
Accepted September 25, 2015
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
... In the PMAT, participants are shown an arrangement of patterns (matrices) and asked to choose the piece that would complete the sequence. As a matrix reasoning task, the PMAT can be used as a measure of fluid intelligence (i.e. a measure of the ability to solve problems in novel situations) and as a proxy for IQ (Swagerman et al., 2016). ...
... This finding is in partial contrast with previous work in schizophrenia samples that found PCET accuracy and reaction time to be heritable as well as a previous study in a population-based twin-family sample that found PMAT accuracy and reaction time to be heritable (Calkins et al., , 2013Greenwood et al., 2007;Gur et al., 2007;Swagerman et al., 2016). ...
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is complex both in its behavioral presentation and in its genetic basis. The use of quantitative behavioral phenotypes instead of the binary, categorical phenotype of diagnosis (Yes ASD, No ASD) has yet to be broadly applied in some areas of ASD research, including ASD genetics. Prior to investigating quantitative ASD-related phenotypes in humans, we reviewed the literature connecting synaptic cell adhesion molecules to social affiliation (a behavior disrupted in autism) in rodent models, and we proposed a mechanistic model. Then, by recruiting autistic adults and their extended family members through the Autism Spectrum Program of Excellence and having them complete a detailed quantitative phenotypic battery, we were able to address the reliability of quantitative phenotyping measures and to start investigating them. We found nearly all of the tested quantitative phenotypes to be heritable across several ASD-relevant behavioral domains – including social communication, repetitive behaviors, and executive functioning. Additionally, we found poor agreement between self-report and informant-report of two such measures (the Social Responsiveness Scale (social communication) and the Behavior Rating Inventory for Executive Function (executive functioning)) among autistic adults. Finally, we looked at the relationships between several relevant quantitative phenotypes, namely measures of overall ASD-related traits, psychological resilience, anxiety, and depression. We found these constructs to be related in such a way that suggests that enhancing resilience may mitigate depression among those high in ASD-related traits. All together, this work points to the promise of a quantitative trait approach in ASD research and highlights the need for several, overlapping measures across multiple behavioral domains for the most thorough understanding of ASD-related behavioral phenotypes.
... Through a rigorous cultural and language adaptation process informed by WHO guidelines, the battery was translated into Setswana (local language) and locally appropriate English [16,22,23]. Variations of the tests composing the adapted PennCNB have been used extensively to identify neurocognitive deficits among pediatric populations [19,[24][25][26]. ...
... Based on the professional consensus procedures, 48 participants were considered cases overall, with 31 demonstrating deficits in executive functioning (13 unclassifiable), 40 in episodic memory (6 unclassifiable), and 3 in sensorimotor speed (17 unclassifiable). The mean MoCA score among all participants was 20 (range: 7-30), and the mean score among cases and controls was 13 (range: 7-25) and 27 (range: [25][26][27][28][29][30], respectively. When applying the − 1.5 SD cut-off to the PennCNB scores, results yielded 13 overall PennCNB-impaired and 59 overall PennCNB-unimpaired. ...
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Children living with HIV (HIV+) experience increased risk of neurocognitive deficits, but standardized cognitive testing is limited in low-resource, high-prevalence settings. The Penn Computerized Neurocognitive Battery (PennCNB) was adapted for use in Botswana. This study evaluated the criterion validity of a locally adapted version of the PennCNB among a cohort of HIV+ individuals aged 10–17 years in Botswana. Participants completed the PennCNB and a comprehensive professional consensus assessment consisting of pencil-and-paper psychological assessments, clinical interview, and review of academic performance. Seventy-two participants were classified as cases (i.e., with cognitive impairment; N = 48) or controls (i.e., without cognitive impairment; N = 24). Sensitivity, specificity, positive predictive value, negative predictive value, and the area under receiver operating characteristic curves were calculated. Discrimination was acceptable, and prediction improved as the threshold for PennCNB impairment was less conservative. This research contributes to the validation of the PennCNB for use among children affected by HIV in Botswana.
... Aberrations in these processes have previously been implicated in neurodevelopmental changes underlying psychosis [62,63] and in altered synapses in the limbic brain areas that drive drinking behavior [64,65]. DPW polygenic risk was also associated with the number of test trials administered to evaluate the social cognition and behavioral function in the psychometric tests related to emotion differentiation and age differentiation [66][67][68]. Finally, PRS for DPW was also positively associated with anxiety-related obsessive-compulsive disorder. ...
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Alcohol drinking and tobacco smoking are hazardous behaviors associated with a wide range of adverse health outcomes. In this study, we explored the association of polygenic risk scores (PRS) related to drinks per week, age of smoking initiation, smoking initiation, cigarettes per day, and smoking cessation with 433 psychiatric and behavioral traits in 4498 children and young adults (aged 8–21) of European ancestry from the Philadelphia neurodevelopmental cohort. After applying a false discovery rate multiple testing correction accounting for the number of PRS and traits tested, we identified 36 associations related to psychotic symptoms, emotion and age recognition social competencies, verbal reasoning, anxiety-related traits, parents’ education, and substance use. These associations were independent of the genetic correlations among the alcohol-drinking and tobacco-smoking traits and those with cognitive performance, educational attainment, risk-taking behaviors, and psychopathology. The removal of participants endorsing substance use did not affect the associations of each PRS with psychiatric and behavioral traits identified as significant in the discovery analyses. Gene-ontology enrichment analyses identified several neurobiological processes underlying mechanisms of the PRS associations we report. In conclusion, we provide novel insights into the genetic overlap of smoking and drinking behaviors in children and young adults, highlighting their independence from psychopathology and substance use.
... Goldenberg et al. (2012),Gur et al. (2012),Mollon et al. (2016),Moore et al. (2015),Sullivan et al. (2016), andSwagerman et al. (2016) Complex cognitionPenn Conditional Exclusion Test (PCET) Determine which of 4 shapes does not belong in groupGoldenberg et al. (2012), Gur et al. (2012), Hartung et al. (2016), Moore et al. (2015), Sullivan et al. (2016), and Swagerman et al. (2016) Penn Line Orientation Test (PLOT) 2 line segments appear on screen and require rotation of 1 segment until lines become parallel Gur et al. (2012), Hartung et al. (2016), Moore TM (2015), and Swagerman et al. (2016) Penn Matrix Reasoning Test (PMAT) Presents series of geometric shapes and requires selection of the shape that completes a pattern Gur et al. (2012), Hartung et al. (2016), Moore et al. (2015), Sullivan et al. (2016), and Swagerman et al. (2016) Sensorimotor/processing speed Finger Tapping Test (CTAP) Press space bar with index finger as quickly as possible for 10 s for 5 trials with alternating hands Goldenberg et al. (2012), Gur et al. (2012), Swagerman et al. (2016)Digit Symbol Substitution Test (DSST) 9 symbol-digit pairs serve as a reference set and require indication whether digit-symbol pairs presented match referenceBearden et al. (2007),Mollon et al. (2016), andSullivan et al. (2016) Motor Praxis Test (MPT) Use computer mouse to click on a green square that appears and disappears in different places on the screen and gets increasingly smallGur et al. (2012) and Swagerman et al. ...
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Human immunodeficiency virus (HIV) infection and in utero exposure increase the risk of neurocognitive deficits, but comprehensive neurocognitive screening is unavailable in settings with high HIV prevalence (e.g., Sub-Saharan Africa). The Penn Computerized Neurocognitive Battery (PennCNB) was culturally adapted and translated for use among children and adolescents in Botswana. To prepare for the implementation process, this study assessed perceptions of acceptability, a leading indicator of implementation success, of the adapted PennCNB among a cohort of HIV+ and HIV-exposed-uninfected (HEU) young people (N = 155, aged 7–17 years) in Gaborone, Botswana. Immediately following completion of the PennCNB, participants completed a three-point Likert scale survey eliciting perspectives of acceptability of the overall PennCNB and the 13 individual subtests and provided open-ended responses to elaborate upon acceptability ratings. Descriptive statistics were calculated, and predictors (age, sex, and PennCNB performance) of unacceptable response were evaluated using logistic regressions. A content analysis was completed on the open-ended responses. Participants reported high acceptability of the overall PennCNB (98%). Of the subtests, the Penn Trailmaking Test, Part A (measuring sensorimotor/processing speed) received the highest acceptability rating (82%), while the Penn Face Memory test (measuring episodic memory) was the least acceptable (40% unacceptable). Age and performance on the PennCNB were associated with an unacceptable response for Fractal N-Back (OR 0.873) and the Penn Line Orientation Test (OR 0.620), respectively. Themes about the content of the cognitive assessment, features of the tests, and participant characteristics were articulated as reasons for reporting the PennCNB subtests as acceptable or unacceptable. Overall, this research offers promise for successful implementation of the PennCNB for use among pediatric populations in Botswana.
... All values, except race, sex, and medications are represented as a mean ± standard deviation. CNB, mean z-scored accuracy across all Penn Computerized Neurocognitive Battery sections as a surrogate for intelligence quotient [66]. NR no response. ...
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Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a multisystem disorder associated with multiple congenital anomalies, variable medical features, and neurodevelopmental differences resulting in diverse psychiatric phenotypes, including marked deficits in facial memory and social cognition. Neuroimaging in individuals with 22q11.2DS has revealed differences relative to matched controls in BOLD fMRI activation during facial affect processing tasks. However, time-varying interactions between brain areas during facial affect processing have not yet been studied with BOLD fMRI in 22q11.2DS. We applied constrained principal component analysis to identify temporally overlapping brain activation patterns from BOLD fMRI data acquired during an emotion identification task from 58 individuals with 22q11.2DS and 58 age-, race-, and sex-matched healthy controls. Delayed frontal-motor feedback signals were diminished in individuals with 22q11.2DS, as were delayed emotional memory signals engaging amygdala, hippocampus, and entorhinal cortex. Early task-related engagement of motor and visual cortices and salience-related insular activation were relatively preserved in 22q11.2DS. Insular activation was associated with task performance within the 22q11.2DS sample. Differences in cortical surface area, but not cortical thickness, showed spatial alignment with an activation pattern associated with face processing. These findings suggest that relative to matched controls, primary visual processing and insular function are relatively intact in individuals with 22q11.22DS, while motor feedback, face processing, and emotional memory processes are more affected. Such insights may help inform potential interventional targets and enhance the specificity of neuroimaging indices of cognitive dysfunction in 22q11.2DS.
... Patients with SCZ and BD show impairment in premorbid intelligence as well as in current intelligence, which involves intelligence decline from the premorbid level [9,12,17]. Intelligence is also substantially heritable with an estimated heritability of approximately 50-70% [18,19]. Large-scale GWASs using nearly 300,000 healthy individuals of general populationbased cohorts have detected more than 100 genome-wide significant loci related to intelligence [20,21]. ...
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Background: Intelligence is inversely associated with schizophrenia (SCZ) and bipolar disorder (BD); it remains unclear whether low intelligence is a cause or consequence. We investigated causal associations of intelligence with SCZ or BD risk and a shared risk between SCZ and BD and SCZ-specific risk. Methods: To estimate putative causal associations, we performed multi-single nucleotide polymorphism (SNP) Mendelian randomization (MR) using generalized summary-data-based MR (GSMR). Summary-level datasets from five GWASs (intelligence, SCZ vs. control [CON], BD vs. CON, SCZ + BD vs. CON, and SCZ vs. BD; sample sizes of up to 269,867) were utilized. Results: A strong bidirectional association between risks for SCZ and BD was observed (odds ratio; ORSCZ → BD = 1.47, p = 2.89 × 10-41, ORBD → SCZ = 1.44, p = 1.85 × 10-52). Low intelligence was bidirectionally associated with a high risk for SCZ, with a stronger effect of intelligence on SCZ risk (ORlower intelligence → SCZ = 1.62, p = 3.23 × 10-14) than the reverse (ORSCZ → lower intelligence = 1.06, p = 3.70 × 10-23). Furthermore, low intelligence affected a shared risk between SCZ and BD (OR lower intelligence → SCZ + BD = 1.23, p = 3.41 × 10-5) and SCZ-specific risk (ORlower intelligence → SCZvsBD = 1.64, p = 9.72 × 10-10); the shared risk (ORSCZ + BD → lower intelligence = 1.04, p = 3.09 × 10-14) but not SCZ-specific risk (ORSCZvsBD → lower intelligence = 1.00, p = 0.88) weakly affected low intelligence. Conversely, there was no significant causal association between intelligence and BD risk (p > 0.05). Conclusions: These findings support observational studies showing that patients with SCZ display impairment in premorbid intelligence and intelligence decline. Moreover, a shared factor between SCZ and BD might contribute to impairment in premorbid intelligence and intelligence decline but SCZ-specific factors might be affected by impairment in premorbid intelligence. We suggest that patients with these genetic factors should be categorized as having a cognitive disorder SCZ or BD subtype.
Schizophrenia is a severe and debilitating psychotic disorder that is highly heritable and relatively common in the population. The clinical heterogeneity associated with schizophrenia is substantial, with patients exhibiting a broad range of deficits and symptom severity. Large-scale genomic studies employing a case–control design have begun to provide some biological insight. However, this strategy combines individuals with clinically diverse symptoms and ignores the genetic risk that is carried by many clinically unaffected individuals. Consequently, the majority of the genetic architecture underlying schizophrenia remains unexplained, and the pathways by which the implicated variants contribute to the clinically observable signs and symptoms are still largely unknown. Parsing the complex, clinical phenotype of schizophrenia into biologically relevant components may have utility in research aimed at understanding the genetic basis of liability. Cognitive dysfunction is a hallmark symptom of schizophrenia that is associated with impaired quality of life and poor functional outcome. Here, we examine the value of quantitative measures of cognitive dysfunction to objectively target the underlying neurobiological pathways and identify genetic variants and gene networks contributing to schizophrenia risk. For a complex disorder, quantitative measures are also more efficient than diagnosis, allowing for the identification of associated genetic variants with fewer subjects. Such a strategy supplements traditional analyses of schizophrenia diagnosis, providing the necessary biological insight to help translate genetic findings into actionable treatment targets. Understanding the genetic basis of cognitive dysfunction in schizophrenia may thus facilitate the development of novel pharmacological and procognitive interventions to improve real-world functioning.KeywordsCognitionEndophenotypeGeneticsSchizophrenia
Human immunodeficiency virus (HIV) infection is prevalent among children and adolescents in Botswana, but standardized neurocognitive testing is limited. The Penn Computerized Neurocognitive Battery (PennCNB) attempts to streamline evaluation of neurocognitive functioning and has been culturally adapted for use among youth in this high-burden, low-resource setting. However, its reliability across measurements (i.e., test–retest reliability) is unknown. This study examined the test–retest reliability of the culturally adapted PennCNB in 65 school-age children (age 7–17) living with HIV in Botswana. Intraclass correlation coefficients (ICCs) for PennCNB summary scores (ICCs > 0.80) and domain scores (ICCs = 0.66–0.88) were higher than those for individual tests, which exhibited more variability (ICCs = 0.50–0.82), with the lowest reliability on memory tests. Practice effects were apparent on some measures, especially within memory and complex cognition domains. Taken together, the adapted PennCNB exhibited adequate test–retest reliability at the domain level but variable reliability for individual tests. Differences in reliability should be considered in implementation of these tests.
Aim of the study: to determine the features of the formation of psychophysiological and cognitive functions in 6-17 year children using a comprehensive and screening software of the original package of the complex “Psychomat”. Materials and methods. A screening examination of 184 apparently healthy 6-17 year schoolchildren was carried out using a complex of psychophysiological tests and original methods for studying higher mental functions (24 tests, 66 parameters). To verify the screening program, a comprehensive examination of 60 apparently healthy schoolchildren of the same age was carried out. Results. The patterns of formation of cognitive and psychophysiological functions in 6-17 year children have been established. No gender differences were found in the analysis of cognitive and psychophysiological functions in children. Significant differences in the rate of formation of psychophysiological functions have been identified in children of primary school age (8-10 years) and are associated mainly with the speed of response and coordination. As the age of children increases, test parameters reflecting the characteristics of perception, memory, attention, analytical and synthetic processes also undergo changes: both the total and average time for completing tasks and the number of errors decrease, and the pace of execution increases. Conclusion. The original software package «Psychomat» allows using comprehensive and screening assessment of both psychophysiological and cognitive functions in 6-17 year children. The screening software as the sensitive method for detecting violations of psychophysiological and cognitive functions in the conditions of a mass examination of children can be used as a test system.
Objective: Comorbidity between posttraumatic stress disorder (PTSD) and substance use disorders (SUD) is common, and both are associated with cognitive dysfunction. However, few studies examine the impact of cognitive deficits on treatment outcomes. Here, we leverage data from a randomized clinical trial of integrated versus phased psychotherapy for SUD and PTSD to examine the relation of cognitive functioning to treatment response. Method: One-hundred and thirteen veterans with co-occurring PTSD and SUD completed Penn Computerized Neurocognitive Battery tests assessing attention, executive control, memory, and spatial processing. Linear mixed-effects models examined interactions between cognitive functioning and time in predicting primary PTSD and SUD outcomes across both treatments. Results: Significant verbal immediate memory by time interactions were found for both PTSD symptoms (p = .01, f 2 = 0.020) and percent heavy drinking or drug use days (p = .004, f 2 = 0.020). There was a significant working memory by time interaction for percent heavy drinking or drug use days (p = .007, f 2 = 0.016). Participants with better verbal memory had greater reductions across time in PTSD symptoms and drinking/drug use, while those with better working memory had lesser reductions in their drinking/drug use across time. Conclusions: Individuals with lower verbal memory functioning had less robust PTSD and SUD symptom reductions in PTSD/SUD psychotherapy, with differences that were generally small in magnitude. Those with better working memory functioning had worse SUD outcomes. Together with prior literature, findings suggest that neurocognitive functioning may impact the effectiveness of PTSD and SUD treatment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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This paper introduces the R package lavaan.survey, a user-friendly interface to design-based complex survey analysis of structural equation models (SEMs). By leveraging existing code in the lavaan and survey packages, the lavaan.survey package allows for SEM analyses of stratified, clustered, and weighted data, as well as multiply imputed complex survey data. lavaan.survey provides several features such as SEMs with replicate weights, a variety of resampling techniques for complex samples, and finite population corrections, features that should prove useful for SEM practitioners faced with the common situation of a sample that is not iid.
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Structural equation modeling (SEM) is a vast field and widely used by many applied researchers in the social and behavioral sciences. Over the years, many software pack-ages for structural equation modeling have been developed, both free and commercial. However, perhaps the best state-of-the-art software packages in this field are still closed-source and/or commercial. The R package lavaan has been developed to provide applied researchers, teachers, and statisticians, a free, fully open-source, but commercial-quality package for latent variable modeling. This paper explains the aims behind the develop-ment of the package, gives an overview of its most important features, and provides some examples to illustrate how lavaan works in practice.
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Objective: The advent of functional MRI (fMRI) enables the identification of brain regions recruited for specific behavioral tasks. Most fMRI studies focus on group effects in single tasks, which limits applicability where assessment of individual differences and multiple brain systems is needed. Method: We demonstrate the feasibility of concurrently measuring fMRI activation patterns and performance on a computerized neurocognitive battery (CNB) in 212 healthy individuals at 2 sites. Cross-validated sparse regression of regional brain amplitude and extent of activation were used to predict concurrent performance on 6 neurocognitive tasks: abstraction/mental flexibility, attention, emotion processing, and verbal, face, and spatial memory. Results: Brain activation was task responsive and domain specific, as reported in previous single-task studies. Prediction of performance was robust for most tasks, particularly for abstraction/mental flexibility and visuospatial memory. Conclusions: The feasibility of administering a comprehensive neuropsychological battery in the scanner was established, and task-specific brain activation patterns improved prediction beyond demographic information. This benchmark index of performance-associated brain activation can be applied to link brain activation with neurocognitive performance during standardized testing. This first step in standardizing a neurocognitive battery for use in fMRI may enable quantitative assessment of patients with brain disorders across multiple cognitive domains. Such data may facilitate identification of neural dysfunction associated with poor performance, allow for identification of individuals at risk for brain disorders, and help guide early intervention and rehabilitation of neurocognitive deficits.
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To further knowledge concerning the nature and nurture of intelligence, we scrutinized how heritability coefficients vary across specific cognitive abilities both theoretically and empirically. Data from 23 twin studies (combined N = 7,852) showed that (a) in adult samples, culture-loaded subtests tend to demonstrate greater heritability coefficients than do culture-reduced subtests; and (b) in samples of both adults and children, a subtest's proportion of variance shared with general intelligence is a function of its cultural load. These findings require an explanation because they do not follow from mainstream theories of intelligence. The findings are consistent with our hypothesis that heritability coefficients differ across cognitive abilities as a result of differences in the contribution of genotype-environment covariance. The counterintuitive finding that the most heritable abilities are the most culture-dependent abilities sheds a new light on the long-standing nature-nurture debate of intelligence.
Life-span developmental psychology involves the study of constancy and change in behavior throughout the life course. One aspect of life-span research has been the advancement of a more general, metatheoretical view on the nature of development. The family of theoretical perspectives associated with this metatheoretical view of life-span developmental psychology includes the recognition of multidirectionality in ontogenetic change, consideration of both age-connected and disconnected developmental factors, a focus on the dynamic and continuous interplay between growth (gain) and decline (loss), emphasis on historical embeddedness and other structural contextual factors, and the study of the range of plasticity in development. Application of the family of perspectives associated with life-span developmental psychology is illustrated for the domain of intellectual development. Two recently emerging perspectives of the family of beliefs are given particular attention. The first proposition is methodological and suggests that plasticity can best be studied with a research strategy called testing-the-limits. The second proposition is theoretical and proffers that any developmental change includes the joint occurrence of gain (growth) and loss (decline) in adaptive capacity. To assess the pattern of positive (gains) and negative (losses) consequences resulting from development, it is necessary to know the criterion demands posed by the individual and the environment during the lifelong process of adaptation.
Background: Cross-sectional studies of samples varying widely in age have found moderate to high levels of shared age-related variance among measures of cognitive and physiological capabilities, leading researchers to posit common factors or common causal influences for diverse age-related phenomenon. Objective: The influence of population average changes with age on cross-sectional estimates of association has not been widely appreciated in developmental and ageing research. Covariances among age-related variables in cross-sectional studies are highly confounded in regards to inferences about associations among rates of change within individuals since covariances can result from a number of sources including average population age-related differences (fixed age effects) in addition to initial individual differences and individual differences in rates of ageing (random age effects). Analysis of narrow age-cohort samples may provide a superior analytical basis for testing hypotheses regarding associations between rates of change in cross-sectional studies. Conclusions: The use of age-heterogeneous cross-sectional designs for evaluating interdependence of ageing-related processes is discouraged since associations will not necessarily reflect individual-level correlated rates of change. Typical cross-sectional studies do not provide sufficient evidence for the interdependence of ageing-related changes and should not serve as the basis for theories and hypotheses of ageing. Reanalyzing existing cross-sectional studies using a sequential narrow-age cohort approach provides a useful alternative for evaluating associations between ageing-related changes. Longitudinal designs, however, provide a much stronger basis for inference regarding associations between rates of ageing within individuals.