Brain & Development xxx (2015) xxx–xxx www.elsevier.com/locate/braindev

Original article

Developmental trajectories for attention and working memory in healthy Japanese school-aged children Chiyomi Egami a, Yushiro Yamashita b,c,d,⇑, Yasuhiro Tada c, Chiduru Anai c, Akiko Mukasa c, Kotaro Yuge b, Shinichiro Nagamitsu b, Toyojiro Matsuishi b,c,d a Department of Nursing, Fukuoka Prefectural University, Japan Department of Pediatrics and Child Health, Kurume University School of Medicine, Japan c NPO Kurume STP, Japan d Cognitive and Molecular Research Institute of Brain Diseases, Kurume University School of Medicine, Japan b

Received 19 July 2014; received in revised form 14 January 2015; accepted 11 February 2015

Abstract Objective: The aim of this study was to investigate the developmental trajectories of attention, short-term memory, and working memory in school-aged children using a 10 min test battery of cognitive function. Methods: Participants comprised 144 typically developing children (TDC) aged 7–12 years and 24 healthy adults, divided according to age into seven groups (12 males and 12 females for each age group). Participants were assessed using CogHealth, which is a computer-based measure composed of five tasks. We measured attention, short-term memory, and working memory (WM) with visual stimulation. Each task was analyzed for age-related differences in reaction time and accuracy rate. Results: Attention tasks were faster in stages from the age of 7–10 years. Accuracy rate of short-term memory gradually increased from 12 years of age and suddenly increased and continued to increase at 22 years of age. Accuracy rate of working memory increased until 12 years of age. Correlations were found between the ages and reaction time, and between ages and accuracy rate of the tasks. Conclusion: These results indicate that there were rapid improvements in attention, short-term memory, and WM performance between 7 and 10 years of age followed by gradual improvement until 12 years of age. Increase in short-term memory continued until 22 years of age. In our experience CogHealth was an easy and useful measure for the evaluation of cognitive function in school-age children. Ó 2015 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Keywords: Attention; Working memory; Short-term memory; Developmental trajectories; Typically developing children

1. Introduction High-level cognitive function is required for humans to live in society, and when cognitive function works ⇑ Corresponding author at: Department of Pediatrics & Child Health, Kurume University School of Medicine, Kurume, Fukuoka 830-0011, Japan. Tel.: +81 942 31 7565; fax: +81 942 38 1792. E-mail address: [email protected] (Y. Yamashita).

efficiently, people are able to pursue an adaptive social lifestyle. Working memory (WM) plays an important role in efficient high-level cognitive function [1], as it temporarily retains information while also processing it. This distinguishes WM from memory, which only retains information. Past studies have reported that WM generally has limited capacity [2], and that this capacity varies according to the individual. WM increases from childhood to adolescence [3], and

http://dx.doi.org/10.1016/j.braindev.2015.02.003 0387-7604/Ó 2015 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Please cite this article in press as: Egami C et al. Developmental trajectories for attention and working memory in healthy Japanese school-aged children. Brain Dev (2015), http://dx.doi.org/10.1016/j.braindev.2015.02.003

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decreases during old age [4]. A study investigating the relationship between WM capacity and response inhibition tasks showed better performance by the high WM capacity group than the low group in antisaccade tasks and Stroop tasks, which involve inhibiting dominant reactions [5]. Research results such as these suggest that WM maturation and decline is closely related to school and social lifestyle. At the core of WM is the attentional control system known as the central executive [1,6]. Attention is thought to comprise components of sustained attention, selective attention and divided and alternating attention [7–9]. Attention, which is thought to mature over a multistage process in which specific components develop during different periods, has been theorized to mature from childhood to adolescence [10–13]. A certain amount of processing resources are required for attentional function of the central executive to work efficiently, and without the necessary capacity it cannot function well. Efficient processing means that attentional function can be used to retain many processing resources. However, poor efficiency where a large amount of resources are used leaves fewer resources available for retention. Thus, the distribution of activation between processing and retention is said to be a trade-off relationship, as WM processes activate information while retaining it [14]. Previous studies have reported that both WM and its central executive system of attention develop during childhood. However, few reports have investigated how WM and attention mature throughout childhood and whether sex-related differences exist. Delays in the development of WM and attention can lead to maladjustment in school and society, and are linked to the onset of secondary disabilities. Therefore, evaluation of age-specific WM and attention development is necessary. The CogHealth battery has been validated for the assessment of cognitive function in attention deficit hyperactivity disorder [15,16]. It comprises five tasks, for example visual attention, memory, and working memory, and this battery requires only 10 min. The CogHealth battery was developed specifically so that repeated performance on the tests does not give rise to improvement in performance in either healthy school aged children or in children with ADHD [16]. These performance measures were used because (1) they are the most appropriate for measuring cognitive changes in children with minimal practice effects, (2) they yield normal distribution, and (3) they allow enough variation in performance to detect declines and improvements in performance. The assessment of cognitive function in children is complicated and time consuming. The aim of this study was to investigate the developmental trajectories of attention, short-term memory, and working memory in typically developing

Japanese children using a 10-min computer-based test battery of cognitive function. 2. Methods 2.1. Participants In total, 144 typically developing children 12 years of age were selected from four primary schools, and 24 healthy adults 22 years of age participated as paid volunteers. They were divided according to age into seven groups of 24 subjects (each comprising 12 males and 12 females). An experienced pediatric neurologist and psychologist obtained the participants’ medical history and performed neurological examination. The participants’ parents were asked to respond to a Strengths and Difficulties Questionnaire (SDQ) regarding their children [17]. The children had no history of visual impairment or neuropsychiatric disorders and were not being administered any medication. Eligibility criteria for TDC included an absence of abnormalities in development, behavior (total needs in SDQ subscale was low), academic difficulty, and neurological findings. Informed consent was obtained from all participants and parents after the details of the study had been fully explained. The Ethical Committee of Kurume University School of Medicine approved the study protocol. 2.2. Tasks All tests were conducted with CogHealth using playing cards that appeared on a computer screen. Each task is shown in Fig. 1. CogHealth is used to assess therapeutic effects [15,16,18,19] and behavioral therapy effects [20] in children with ADHD. It assesses five types of cognitive functions including visual attention and WM, which can be used to conduct an objective assessment, and it takes approximately 10 min to complete the test. The cognitive paradigms operationally defined in the brief CogHealth battery (other than the monitoring task) are that they have an acceptable construct and criterion validity in a neuropsychological context [21,22]. One of the advantages of CogHealth is that it can be used repeatedly because it has no learning effects. Because of these features, we felt that CogHealth was suitable as a tool for measuring and monitoring therapeutic effects, including behavioral therapy and pharmacotherapy in individuals and groups during childhood when cognitive function is developing. (1) Detection: A task measuring sustained visual attention. (2) Identification: A task measuring selective visual attention function.

Please cite this article in press as: Egami C et al. Developmental trajectories for attention and working memory in healthy Japanese school-aged children. Brain Dev (2015), http://dx.doi.org/10.1016/j.braindev.2015.02.003

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Fig. 1. Cognitive function measures: CogHealth (5 tasks).

(3) One-card-learning: A task measuring short-term visual memory. (4) One-back-learning: A task measuring visual WM. (5) Monitoring: A task measuring divided visual attention function.

2.3. Procedure The participating children sat facing a laptop computer (HP compaq6730s, OS: Windows XP, CPU: Intel Celeron T1600, Memory 2 GB with a 15.4 inch screen) with a keyboard that was placed within reach. The tasks required them to press “K” for “yes” and “D” for “no.” There is no practice effect after taking the test a second time if this method of testing is practiced once [15]. Therefore, participants performed the tasks after practicing them briefly. 2.4. Data and statistical analysis All 168 participants were subject to analysis since they were all able to complete the CogHealth tasks. Data were analyzed for reaction times and accuracy rates. Arcsin values were used as accuracy rates, and reaction time was the mean reaction time when correct answers were given. To clarify the effect of age, we verified whether there was interaction in models that included interactions with age for each task. To avoid assuming normality, response variables were ranked and an analysis of variance and Tukey’s Multiple Comparison Test were performed on the ranked data.

When no interaction existed, an analysis was conducted using a model without interaction. When sex had no significant effect, it was excluded from the analysis. Spearman’s partial rank correlation coefficient corrected for age was calculated for the task correlation coefficients. We used Spearman partial rank correlation coefficient for analyzing correlation between age and the task, and for calculating the correlation coefficient of age and the task. Statistical processing was conducted using the SAS system and all levels of significance were set at p < 0.05. 3. Results 3.1. Task correlations For age and task correlation coefficients, correlations were observed for reaction time for detection and identification and one-back-learning (p < 0.01) (Table 1), one-card-learning and monitoring (p < 0.05). Correlations were observed for accuracy rates in onecard-learning and one-back-learning (p < 0.05). 3.2. Detection Main effects of age were observed in reaction times [age: F = 35.97, p < 0.001] (Fig. 2). Reaction times were significantly slower in the 7- and 8-year-old groups compared with the 10-year and older groups (p < 0.001), and significantly slower in the 9-year-old group than the 10- (p < 0.01), 11-, 12-year-old, and adult (p < 0.001) groups.

Please cite this article in press as: Egami C et al. Developmental trajectories for attention and working memory in healthy Japanese school-aged children. Brain Dev (2015), http://dx.doi.org/10.1016/j.braindev.2015.02.003

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0.219 0.631*

Reaction time Accuracy

0.536* 0.748**

Reaction time

A main effect of age was observed in reaction times [age: F = 48.69, p < 0.001] (Fig. 3). Reaction times were significantly slower in the 7-year-old group than the 8- (p < 0.05), 9-, 10-, 11-, 12-year-old, and adult (p < 0.001) groups, and was significantly slower in the 8-year-old group than in the 10-year and older groups (p < 0.001). Reaction times were also significantly slower in the 9-year-old group than the 10 (p < 0.01), 11-, 12-year-old, and adult (p < 0.001) groups. In accuracy rates, a main effect of age was observed [age: F = 8.75, p < 0.001]. Accuracy rates were significantly lower in the 7-year-old group than the 12-year-old (p < 0.01) and adult (p < 0.001) groups, and significantly lower in the 8-, 9-, 10-, and 11-year-old groups than the adult group (p < 0.001).

0.541*

Accuracy

3.3. Identification

p < 0.05. p < 0.01. **

*

Reaction time

0.582* 0.477

Accuracy Reaction time

0.799**

3.5. One-back-learning

0.381

Accuracy Reaction time

A main effect of age was observed in reaction times [age: F = 20.60, p < 0.001] (Fig. 4). Reaction times were significantly slower in the 7- and 8-year-old groups than the 10-year and older groups (p < 0.001), and significantly slower in the 9-year-old group than the 10-, 11- (p < 0.001), 12-year-old (p < 0.01), and adult (p < 0.001) groups. In accuracy rates, a main effect of age was observed [age: F = 20.53, p < 0.001]. Accuracy rates were significantly lower in the 7- and 9-year-old groups than the 12-year-old and adult (p < 0.001) groups, and significantly lower in the 8-, 10-, and 11-year-old groups than the 12-year-old (p < 0.05), and adult (p < 0.001) groups. Accuracy rates were also lower in the 12-year-old group than the adult group (p < 0.01).

0.753**

One-back-learning One-card-learning Identification Detection

Table 1 Correlation between ages and the task.

A main effect of age was observed in accuracy rates [age: F = 6.50, p < 0.01]. Accuracy rates were significantly lower in the 7-year-old group than the 10(p < 0.05), 11- (p < 0.001), 12-year-old (p < 0.05), and adult (p < 0.001) groups, and was significantly lower in the 8-year-old group than the adult group (p < 0.001).

3.4. One-card-learning

Age

Monitoring

Accuracy

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A main effect of age was observed in reaction times [age: F = 34.61, p < 0.001] (Fig. 5). Reaction times were significantly slower in the 7-year-old group than the 9(p < 0.05), 10–11-, 12-year-old, and adult (p < 0.001) groups, and significantly slower in the 8-year-old group than the 10-year and older groups (p < 0.001). Reaction times were also significantly slower in the 9-year-old group than the 10- (p < 0.05), 11- (p < 0.01), 12-yearold, and adult (p < 0.001) groups, significantly slower in the 10- and 11-year-old groups than the adult group

Please cite this article in press as: Egami C et al. Developmental trajectories for attention and working memory in healthy Japanese school-aged children. Brain Dev (2015), http://dx.doi.org/10.1016/j.braindev.2015.02.003

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Fig. 2. Reaction time and accuracy in detection. The left figure shows reaction time and the right figure shows accuracy. Asterisks (*) indicate significant differences between the age groups. *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 3. Reaction time and accuracy in identification. The left figure shows reaction time and the right figure shows accuracy. Asterisks (*) indicate significant differences between the age groups. *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 4. Reaction time and accuracy in one-card-learning. The left figure shows reaction time and the right figure shows accuracy. Asterisks (*) indicate significant differences between the age groups. *p < 0.05, **p < 0.01, ***p < 0.001.

Please cite this article in press as: Egami C et al. Developmental trajectories for attention and working memory in healthy Japanese school-aged children. Brain Dev (2015), http://dx.doi.org/10.1016/j.braindev.2015.02.003

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Fig. 5. Reaction time and accuracy in one-back-learning. The left figure shows reaction time and the right figure shows accuracy. Asterisks (*) indicate significant differences between the age groups. *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 6. Reaction time and accuracy in monitoring. The left figure shows reaction time and the right figure shows accuracy. Asterisks (*) indicate significant differences between the age groups. *p < 0.05, **p < 0.01, ***p < 0.001.

(p < 0.001), and significantly slower in the 12-year-old group than the adult group (p < 0.01). In accuracy rates, a main effect of age was observed [age: F = 9.61, p < 0.001]. Accuracy rates were significantly lower in the 7-year-old group than the 10- (p < 0.01), 11-, and 12-year-old, and adult (p < 0.001) groups, significantly lower in the 8-year-old group than 12-year-old and adult (p < 0.01) groups, and significantly lower in the 9-year-old group than the 12-year-old and adult (p < 0.05) groups. 3.6. Monitoring A main effect of age was observed in reaction times [age: F = 17.29, p < 0.001] (Fig. 6). Reaction time was significantly slower in the 7-year-old group than the 10- (p < 0.01), 11-, 12-year-old, and adult (p < 0.001) groups, significantly slower in the 8-year-old group than the 10- (p < 0.05), 11-, 12-year-old, and adult (p < 0.001)

groups, significantly slower in the 9-year-old group than the 11- (p < 0.05), 12-year-old, and adult (p < 0.001) groups, and significantly slower in the 10-year-old group than the adult group (p < 0.05). In accuracy rates, no main effect of age was observed [age: F = 1.40, n.s.]. 4. Discussion This study examined developmental trajectories using CogHealth to clarify the characteristics of attention, short-term memory, and WM in children aged 7–12 years. We examined a correlation between task and age. In detection, identification, and monitoring, a correlation was found between age and reaction time. Also, in the one-card-learning and one-back-learning, a correlation was found between age and reaction time and accuracy rates. According to previous studies, validity in reaction

Please cite this article in press as: Egami C et al. Developmental trajectories for attention and working memory in healthy Japanese school-aged children. Brain Dev (2015), http://dx.doi.org/10.1016/j.braindev.2015.02.003

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time in detection and identification tests were found, and the validity in accuracy rates in one-card-learning and one-back-learning test were demonstrated [21,22]. Therefore, validity was confirmed, and a correlation with age was found by using an analysis index. For the detection task, face-down playing cards were turned face-up at irregular time intervals. When they were face-up, subjects pressed a key, which required sustained attention. Our results which indicated that the 7–10-year-olds had slower reaction times than the other groups, which suggested that the sustained attention of the 7–10-year-olds was less mature than the other groups, and that it was still under development until around 10 years of age. This result was consistent with previous studies [10,11,23]. The identification task for selective attention found that reaction times became gradually faster from 7 to 10 years of age. Development is staged up to 7 and 9 years of age. Our results suggest that selective attention matures from 7 to 10 years of age. In previous studies it was reported that reaction time for selective attention matures until 10 years of age [24], and that it develops quickly from 5 to 8 years and then slowly from 9 to 12 years of age [12]. There was no previous study regarding the validity of the monitoring task. The monitoring task for divided attention revealed that reaction times increased in speed from 7 to 10 years, suggesting that reaction time leveled off from 11 years of age onward. Our results support the findings of previous studies, suggesting that as age increases, people develop so as to process certain focus points of information, and divide their attention and shift it to appropriate areas while inhibiting unnecessary information [25,26]. It appears that the frontal lobe, which is involved in high-level attentional function, develops with increasing speed from 7 to 10 years of age [24]. The frontal lobe is involved in selective attention and divided attention [26,27] and reflects brain activity maturation. Reaction times for monitoring were faster than for identification. Factors for this appeared to be the involvement of the neural pathways including a topdown pathway originating in the dorsal frontoparietal network and a bottom-up pathway originating in the ventral-temporal frontoparietal network in visual attention [28–32]. As the identification task used color, and the monitoring task used movement, the involvement of visual information processing [32] reflected the neural mechanisms of selective attention and divided attention. The fact that no correlation was observed in sustained attention, selective attention, or divided attention suggests that development of attention is a multistage maturation process that occurs at different points depending on each individual, which is consistent with the findings from previous studies [3].

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One-card-learning measures short-term memory, which is the temporary retention of information, and corresponds to an episodic buffer of the multicomponent working memory model [1,6]. Results indicated significantly higher accuracy rates in the 12-year-old group and the adult group than other groups, suggesting stages of maturation from 7 to 10 years and rapid maturation at 12 years of age and 22 years of age. Previous studies reported that memory capacity increases [33,34] and processing speed decreases [35] with development until after puberty. Our study result was consistent with the findings of previous studies. The one-back-learning task that assesses working memory focuses on the function of information processing while performing cognitive tasks. Accuracy rates were higher in the 12-year-old group and adult group than the 7–9 year old group. These results indicate that WM gradually develops with age, consistent with the findings from previous studies [3]. Because episodic memory is dependent on the hippocampus [36] and the existence of a dopamine-related gene associated with WM (NTSR1 gene) it is thought that the hippocampus is related to WM [37]. Based on these results which showed that 12-year-olds had higher accuracy rates than all the other groups in one-card-learning, and the fact that previous research has shown that WM increases until adolescence, we deduced that episodic memory and WM increase in accordance with the maturation of the hippocampus. A relationship has also been reported between attention and WM [38]. The dorsolateral prefrontal cortex (DLPFC) and the anterior cingulate cortex (ACC) are involved in the attentional control system, with the neural network coordinating control attention for WM tasks. Because the DLPFC provides adequate top-down behavioral support for the task and the ACC deals with conflicting reactions, the DLPFC and ACC are thought to play roles in monitoring performance when stronger control is required [39]. The age-related differences may have reflected frontal lobe maturation and coordination between the DLPFC and the ACC. From these facts, we can deduce that attention, short-term memory, and WM work together to perform high-level cognitive function. In the present study w examined a correlation of age and index of the task. However, evaluation of the validity will be necessary in a future study regarding the reaction time in one-card-learning, one-back-learning, and monitoring task. A previous study did not provide this validity although a correlation was found [21,22]. In conclusion, visuospatial sustained attention, selective attention, divided attention, short-term memory, and WM of 7–12-year-old children indicated that each function underwent a stage of rapid maturation from 7 to 10 years of age and a stage of gradual maturation until 12 years of age. CogHealth was an easy and useful

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measure for the evaluation of cognitive function in school age children. Further studies with expanded number of TDC until puberty and evaluation of children with developmental disorders, epilepsy, and head trauma are required to clarify the clinical significance of CogHealth. Conflict of interest None of the authors has any conflict of interest to disclose.

Acknowledgements This study was supported by Intramural Research Grant (22-6, 25-6; Clinical Research for Diagnostic and Therapeutic Innovations in Developmental Disorders) for Neurological and Psychiatric Disorders of NCNP and the Grant-in-Aid for Scientific Research C (23591519) from the Ministry of Education, Culture, Sports, Science, and Technology of Japan. We also thank Dr. Paul Maruff for his editorial advice.

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Developmental trajectories for attention and working memory in healthy Japanese school-aged children.

The aim of this study was to investigate the developmental trajectories of attention, short-term memory, and working memory in school-aged children us...
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