attention deficit disorder with hyperactivity - child - memory
transtorno do déficit de atenção com hiperatividade - criança - memória
Attention deficit/hyperactivity disorder (ADHD) is a frequent condition in childhood
affecting around 5% of school-age children[1]. Early cross-sectional studies indicated 4% to 5% reduction in total cerebral and
cerebellar volumes in children and adolescents with ADHD, compared to typically developing
children (TDC)[2]. Later findings suggested that the decrease is mainly due to reduced grey and white
matter volumes and other regional abnormalities in the prefrontal cortex, especially
the orbitofrontal and dorsolateral prefrontal cortices, basal ganglia, and cerebellum[3]. Attention deficit/hyperactivity disorder is a neurodevelopmental disorder with
a remission rate of approximately 60% during late adolescence, meaning that symptoms
subdue in a majority of cases, but not all. It has been shown that ADHD symptoms are
correlated to the rate of cortical thinning in the medial and dorsolateral prefrontal
cortex[4].
Although not necessary for a clinical diagnosis, neuropsychological tests provide
a better understanding of the cognitive profile of ADHD patients in clinical practice
as well as contributing to a better understanding of the cognitive deficits of the
disorder. Working memory (WM) deficits are well described in a myriad of disorders
including ADHD[5]and appear to be associated with a worse outcome, even when there is no comorbid
learning disorder. The WM provides short-term storage and processing of sensory information.
It has a critical role in guiding everyday behavior, underlying the ability to perform
complex tasks such as learning, comprehension, reasoning, and planning[6]. It is noteworthy that behaviors associated with WM deficits might be the main complaints
that lead ADHD individuals to seek treatment in specialized centers[7].
The Digit Span subtest from the Wechsler Intelligence Scale for Children (WISC-III)[8]is the most commonly used test in clinical practice to assess working memory, although
some authors[9]have questioned its sensitivity when milder deficits are present[10].
The Digit Span test consists of progressively lengthier forward and backward repetitions
of numbers[8],[11]. The forward condition is considered a measure of the phonological loop whereas
the backwards condition is considered a measure of central executive (i.e., working memory ) given that it demands both storage and manipulation in order to retain and repeat
the number in reverse order[12].
Our group has previously demonstrated the importance of this test in discriminating
between ADHD children and children with complaints of low academic performance referred
for neuropsychological evaluation[13].
Since patients with ADHD have reduced cortical thickness[14], the aim of the present study is to investigate the correlation between WM and cortical
thickness in ADHD children. To our knowledge, there is no previous correlation between
those variables.
METHODS
After the institutional review board approval and parents’ informed consent signature,
17 children of both genders, aged between seven and 10 years, were selected from the
ADHD outpatient clinic of the Children’s Hospital of the Federal University of Rio
de Janeiro. All of them were drug-naïve and were diagnosed using DSM-IV criteria[15]. The ADHD module of the Kiddie-Schedule for Affective Disorders and Schizophrenia
was used in order to confirm the diagnosis[16],[17]. The DSM-5 criteria[18]had not yet been published when the study took place. As the new changes have occurred
in relation to age of onset of symptoms (up to 12 years old) and a lower cutoff criterion
for adults, our sample was not affected.
Sixteen gender- and age-matched TDC were selected from the elementary school of the
same university. The K-SADS questionnaire was administered in order to exclude an
ADHD diagnosis. Both groups underwent neuropsychological evaluation, including intelligence
quotient (IQ) measurement and the Digit Span test from WISC-III.
A 3.0 Tesla scanner (Magnetom Verio, Siemens, Germany) with a 12 channel head coil
was used to obtain MRI data. The imaging protocol images 3D gradient echo T1-sagittal
plane, T2-weighted coronal plane, 3D FLAIR images in the sagittal plane and diffusion
tensor (DTI) orthogonal directions in 30 gradients. Images were transferred to a workstation
(CENTOS 4.9, Linux) with 8 GB of RAM memory and two Quad-Core Intel Xeon processors
(2 x 3.2 GHz). FreeSurfer version 5.0.0 was used to perform cortical reconstruction
(http://surfer.nmr.mgh.harvard.edu). The procedures included motion correction; removal
of non-brain tissue using a hybrid watershed/surface deformation procedure; automated
Talairach transformation; segmentation of subcortical white matter and deep gray matter
structures, including the thalamus, hippocampus, amygdala, caudate, putamen, and ventricles;
intensity normalization; tessellation of the gray matter/white matter boundary; automated
topology correction; skull stripping and surface deformation and inflation of the
cerebrum[18]. FreeSurfer software provided correction for motion in all images, reducing interference
from movement during acquisition. Besides, an experienced neuroradiologist (ELG) and
medical physicist (TTAK) accompanied all examinations and motion artifacts were excluded.
Cortical thickness maps were calculated for each subject. The mean cortical thickness
in regions-of-interest in the patient group and control group were computed and statistically
compared (p < 0.01) by a single-binary application included in the FreeSurfer distribution,
Qdec, based on a General Linear Model. Correction for multiple comparisons was made
by Qdec using Monte-Carlo simulation (p = 0.05). Procedures for the accuracy of cortical
thickness measurements were validated with histological analysis[19],[20]. Age was included as a covariate[21].
It is noteworthy that groups were corrected in the common error region of FreeSurfer,
which included of the skull as gray matter. The criterion for surface reconstruction
was that the red lines (gray matter) should cover the gray matter without invading
the areas of white matter. All individuals suffered minor corrections of this segmentation.
About 35% of both patients and controls underwent correction. Groups were blinded
to the medical physicist.
The Monte Carlo method provided correction for multiple comparisons and four brain
cortical regions were appraised: left superior, medial and inferior temporal cortices,
and left inferior parietal cortex. The non-parametric Mann-Whitney test was performed
to analyze the difference between ADHD and TDC with regard to the measure of cortical
thickness and the results of the Digit Span subtests – digit forwards and digit backwards.
The Benjamini-Hochberg correction was used to calculate the false discovery rate for
each of the p-values ([Table 1]).
Table 1
Values of cortical thickness and Digit Span scores in children with attention deficit/hyperactivity
disorder (ADHD) and typically developing children (TDC).
|
Region
|
ADHD
|
TDC
|
Mann-Whitney U
|
p-value
|
BH corrected (p-value)
|
|
Left superior temporal cortex*
|
2.5
|
2.9
|
30.5
|
0.000
|
0.000
|
|
Left medial temporal cortex*
|
3.1
|
3.5
|
22.0
|
0.000
|
0.000
|
|
Left inferior temporal cortex*
|
2.9
|
3.4
|
65.0
|
0.010
|
0.015
|
|
Left inferior parietal cortex*
|
2.5
|
3.0
|
21.0
|
0.000
|
0.000
|
|
Forward Digit Span
|
7.2
|
7.1
|
134.5
|
0.958
|
0.956
|
|
Backwards Digit Span
|
3.6
|
4.2
|
84.0
|
0.063
|
0.061
|
* in mm; BH: Benjamini-Hochberg correction for multiple comparisons.
RESULTS
The TDC and ADHD children were comparable in terms of age, gender, and intelligent
quotient (IQ), as shown in[Table 2].
Table 2
Comparison between the variables of age, gender and intelligence quotient (IQ) in
children with attention deficit/hyperactivity disorder (ADHD) and typically developing
children (TDC).
|
Variable
|
ADHD
|
TDC
|
p-value
|
|
Gender (Male/Female)
|
13/abr
|
12/abr
|
1.000
|
|
Age
*
|
8 (1.2)
|
9 (1.3)
|
0.368
|
|
IQ
*
|
105 (13.6)
|
106 (17.5)
|
0.639
|
*
Medial (standard deviation).
The difference between TDC and ADHD children was significant for the four cortical
regions mentioned above. Nevertheless, Digit Span scores were not statistically significant
different between the groups of children.
Box-plots depicting differences in the groups’ distributions according to cortical
thickness of the left superior, medial and inferior temporal cortices, and left inferior
parietal regions were represented in
[Figure 1].
Figure 1 Box-plots depicting differences in distributions between the groups according to
cortical thickness of left superior, medial and inferior temporal cortices, and left
inferior parietal regions.ADHD: Attention deficit/hyperactivity disorder; TDC: typically
developing children.
Considering that no statistically significant difference between Digit Span scores
of TDC and ADHD children was detected, scatter-plots showing correlations between
cortical thickness and these scores were built taking both groups into account together
([Figure 2]).
Figure 2 Scatter-plots showing correlations between cortical thickness and Digit Span scores
(Forward and Backward) in both groups together.
[Table 3]shows the correlation between the cortical thickness of each brain area selected
and the values obtained in the Digit Span subtests (Spearman’s correlation coefficient).
Here, we observed a direct association between the scores on the Backwards Digit Span
and thickness of the left medial temporal cortex (Spearman’s correlation coefficient
= 0.499; significant at the 0.01 level; 2-tailed). To a lesser extent, we observed
the same association with the left inferior temporal cortex (Spearman’s correlation
coefficient = 0.388; significant at the 0.05 level; 2-tailed).
Table 3
Values of Spearman’s correlation coefficient between cortical thickness of each brain
area selected and the values obtained in the Digit Span test.
|
Region
|
Forward digit span
|
Backwards digit span
|
|
Left superior temporal cortex
|
|
Spearman’s coefficient
|
-0.196
|
0.322*
|
|
Sig. (2-tailed)
|
0.274
|
0.067
|
|
BH corrected p-value
|
0.419
|
0.089
|
|
Left medial temporal cortex
|
|
Spearman’s coefficient
|
-0.190
|
0.499**
|
|
Sig. (2-tailed)
|
0.288
|
0.003
|
|
BH corrected p-value
|
0.419
|
0.012
|
|
Left inferior temporal cortex
|
|
Spearman’s coefficient
|
-0.181
|
0.388*
|
|
Sig. (2-tailed)
|
0.314
|
0.026
|
|
BH corrected p-value
|
0.419
|
0.052
|
|
Left inferior parietal cortex
|
|
Spearman’s coefficient
|
-0.026
|
0.136
|
|
Sig. (2-tailed)
|
0.886
|
0.451
|
|
BH corrected p-value
|
0.886
|
0.451
|
Sig.: significant; BH: Benjamini-Hochberg; *Correlation is significant at the 0.10
level (2-tailed); **Correlation is significant at the 0.05 level (2-tailed); ***Correlation
is significant at the 0.01 level (2-tailed).
DISCUSSION
Our findings have shown a direct relationship between cortical thickness of the left
medial temporal cortex and working memory, evaluated through the Backwards Digit Span
test. The correlation of WM with the left inferior temporal cortical thickness was
also observed, but to a lesser extent. No correlation was observed between the Forward
Digit Span and cortical thickness in these brain regions.
This last result is in accordance with previous studies that discuss the validity
of using both Digit Span conditions – forwards and backwards – separately, since they
involve different neuropsychological circuits[22]and only the reverse condition addresses working memory[13].
Traditionally, the frontal lobes are recognized as responsible for the control of
complex cognitive processes such as decision-making, planning and sustained attention[23]. However, more recently, there has been strong evidence indicating the contribution
of the medial temporal lobe to WM[24],[25],[26],[27]. In a less robust manner, the inferior temporal lobe’s role in WM has also been
demonstrated[28].
According to the classic definition, WM relies on three interconnected subsystems:
the phonological loop, responsible for the initial processing and storage of verbal
information, the visual sketchpad, responsible for the initial processing of nonverbal
information; and the episodic buffer, responsible for the connection of the information
between the former systems[29]. The frontotemporal pathways play an important role in integrating these three subsystems
of WM[30].
The role of the medial temporal lobe in WM is not fully elucidated, as there are studies
demonstrating that this region influences WM only when the task depends more on long-term
memory processes[31].
Our sample size should be considered a limitation to the study and the results may
not be generalized. A larger sample might show a statistically significant difference
between the Backwards Digit Span scores of TDC and ADHD children. Although many steps
were taken to minimize movement bias during examinations, head motion in ADHD patients
can be considered a problem and a limitation of this study.
This pilot study was not able to confirm that working memory problems can differentiate
ADHD from TDC. Nevertheless, our results suggest, for the first time, a direct correlation
between the Backwards Digit Span and left medial temporal cortical thickness.
