Clinical Neurophysiology xxx (2015) xxx–xxx

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Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph

The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children Andreas Mierau a,⇑, Moritz Felsch b, Thorben Hülsdünker a, Julia Mierau a, Pola Bullermann a, Britta Weiß a, Heiko K. Strüder a a b

Institute of Movement and Neurosciences, German Sport University Cologne, Germany Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne, Germany

a r t i c l e

i n f o

Article history: Accepted 13 March 2015 Available online xxxx Keywords: Brain Development Locomotion Motor coordination Cognition Childhood

h i g h l i g h t s  Sensorimotor and working memory performance are correlated in young children.  Brain’s global functional architecture (BGFA) correlated with locomotor skills.  However, BGFA did not significantly correlate with working memory performance.

a b s t r a c t Objective: The aim of this study was to identify the interrelation between sensorimotor abilities, cognitive performance and individual alpha peak frequency (iAPF), an EEG marker of global architectural and functional properties of the human brain, in healthy preschool children. Methods: 25 participants completed a one minute eyes-closed EEG recording, two cognitive tests assessing processing speed and visual working memory and a sensorimotor test battery. Results: We found positive correlations between selective sensorimotor abilities and iAPF; however, no significant correlations were observed between iAPF and cognitive performance. Specifically, locomotor skills correlated with iAPF across all cortical regions, except for the occipital cortex. Furthermore, a close relationship was found between sensorimotor and cognitive performance indicating that children with improved sensorimotor abilities were faster and/or more accurate in cognitive task performance. The cumulative pattern of our results indicates that a close relationship exists between sensorimotor and cognitive performance in young children. However, this relationship is dissociated from the iAPF. Conclusion: In contrast to adults, in young children the iAPF is related to locomotor skills and not to cognitive processing speed or visual working memory function. Significance: The global architectural and functional properties of the brain are closely related to locomotor skills during development. Ó 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction Numerous experiments have established a close relationship between sensorimotor and cognitive development (Piek et al., 2008; Roebers and Kauer, 2009; Niederer et al., 2011). However, the neurobiological mechanisms underlying this relationship are inadequately studied, and most of the current discussion in this

⇑ Corresponding author at: Institute of Movement and Neurosciences, German Sport University, 50933 Cologne, Germany. Tel.: +49 221 4982 4060; fax: +49 221 4973454. E-mail address: [email protected] (A. Mierau).

context is based upon extrapolation from studies on children with neurological disorders. This research indicates that neurodevelopmental disorders such as attention-deficit hyperactivity disorder (ADHD) and developmental coordination disorder (DCD) are typically characterised by both, abnormal sensorimotor and cognitive control (Diamond, 2000; Zwicker et al., 2009). It has been argued this co-occurrence of sensorimotor and cognitive deficits in children with developmental problems is highly suggestive of neuropathology of the cerebellum. However, given the heterogeneity of these disorders, other sources such as the basal ganglia, the parietal lobe, the corpus callosum and the prefrontal cortex may also be involved (Diamond, 2000; Zwicker et al., 2009). More recent

http://dx.doi.org/10.1016/j.clinph.2015.03.008 1388-2457/Ó 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Please cite this article in press as: Mierau A et al. The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.03.008

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A. Mierau et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

studies used functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to identify the neural correlates of DCD and ADHD. The results of these studies support the existence of both common and distinct neurobiological substrates underlying motor and attention problems. Specifically, it was found that functional connectivity of neural motor networks is disrupted in children with DCD and/or ADHD (McLeod et al., 2014). Furthermore, microstructural alternations in the corpus callosum were associated with difficulties in motor and attention functioning. However, these alternations are functionally and regionally distinct (Langevin et al., 2014). Notwithstanding the above mentioned evidence, more research is needed addressing the link between sensorimotor and cognitive development using neurobiological measures in both healthy children and those with neurodevelopmental disorders across different ages. However, it is a central problem in research investigating the brain–behaviour relationship during infancy and early childhood that neuroimaging using techniques such as (f)MRI, DTI and positron emission tomography (PET) is limited due to movement restriction and feelings of discomfort. In addition, PET involves the injection of a radioactive isotope. Given these restrictions, electroencephalography (EEG) is probably the most frequently used neuroimaging technique in infants and children, as its application is comparatively unproblematic. Among the quantitative EEG parameters, the individual alpha peak frequency (iAPF) was found to be the best signature of brain maturation (Valdés et al., 1990). The iAPF is the dominant oscillatory frequency in the human EEG during relaxed wakefulness, and it is considered a marker of global architectural and functional properties of the human brain (Grandy et al., 2013a). It increases from infancy to adulthood, then decreases with age analogue to changes in brain architecture and general cognitive abilities (Klimesch, 1999). The iAPF shows large interindividual variability and has been shown to correlate with a range of cognitive tasks in adults. For example, adult individuals with higher iAPF show shorter reaction times (Jin et al., 2006), better working memory scores (Richard Clark et al., 2004; Grandy et al., 2013a) and superior memory performance (Klimesch et al., 1993). Although the iAPF is consistently discussed as a neurophysiological marker of brain maturation, the relationship between iAPF and behaviour during childhood development remains unknown. The primary aims of the present study are: (1) to establish the relationship between sensorimotor abilities and cognitive performance in young children, (2) to identify how these two domains relate to the iAPF, and (3) when indicated, to analyse whether the relationship between sensorimotor abilities and cognitive performance is mediated by the iAPF. Based on previous research, it was hypothesised that a close relationship exists between sensorimotor abilities and cognitive performance. In addition, both sensorimotor and cognitive performance should correlate with iAPF if the two domains are fundamentally interrelated and develop in parallel (Diamond, 2000). However, given the inconclusive literature about the directional nature of the relationship between sensorimotor and cognitive development, alternative hypotheses (e.g. significant correlation between iAPF and sensorimotor, but not cognitive performance or vice versa) could also be posed.

2. Methods

addition, parents had to confirm their child was in good health and did not have any overt medical conditions or a documented history of developmental delays. The children and their parents were informed about the intention and procedure of the study and their verbal (children) and written (parents) consent was obtained. The study was designed in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the German Sport University in Cologne. 2.2. Measures 2.2.1. EEG data acquisition and analysis Each participant’s eyes-closed resting state EEG (Brain Products, Munich, Germany) was recorded for 1 min. Children were instructed to sit relaxed with their hands placed on their thighs and to try not to move. Using an elastic cap (ActiCap; Brain Products, Munich, Germany), 20 scalp electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, Oz, O2) were placed according to the international 10:20 system (Jasper, 1958). In addition, one electrooculographic electrode was placed laterally below the right eye to detect eye movement. The electrical reference and the ground electrode were located on position FCz and AFz, respectively. Electrode impedance was kept below 5 kX and data was sampled with 256 Hz. EEG data were analysed using the Brain Vision Analyzer 2 (Brain Products, Germany) software package and scripts based on EEGlab (Delorme and Makeig, 2004). In a first step, data were digitally band pass filtered using an IIR butterworth filter (low pass: 40 Hz, 48 db/oct; high pass: 1 Hz, 48 db/oct) and segmented into 4 s epochs. All segments were baseline corrected and a semiautomatic artefact rejection algorithm (maximal allowed voltage step: 100 lV; maximal allowed voltage difference within 30 ms: 150 lV; maximal/minimal allowed amplitude within each segment: ±200 lV) was applied to detect gross artefacts. Remaining artefacts were corrected applying independent component analysis (ICA). After ICA-based artefact correction, the data were rereferenced to a common average reference. The spectral power of each segment was calculated using fast Fourier transformation (FFT) (resolution: 0.25 Hz; 20% Hanning window) and data were subsequently averaged across segments. The iAPF was defined as the frequency bin displaying the highest power value within the frequency range 5–12.5 Hz and was averaged across electrodes to form the following five regions: frontal (Fp1, Fp2, F7, F3, Fz, F4, F8, FCz), central (C3, Cz, C4), parietal (P3, Pz, P4), occipital (O1, Oz, O2) and temporal (T7, T8, P7, P8). While the iAPF is traditionally defined as the peak power value within the traditional adult alpha frequency range (7.5–12.5 Hz), in this study the lower boundary was corrected to account for reduced peak frequencies in children when compared to adults (cf. Klimesch, 1999). 2.2.2. Cognitive performance Cognitive performance was assessed using the computerised Vienna Test System (VTS) for neuropsychological assessment (Schuhfried, Austria, http://www.schuhfried.com/viennatestsystem10/vienna-test-system-vts/). For each child, the first test presentation started with automatic and standardised test instructions, including a practice period in order to ensure the tasks were fully understood. The participants were asked by the examiner to focus on the task and as a reward for attendance allowed their choice from a range of toys or sweets after completion of the tests.

2.1. Participants and ethical statement 25 children (9 females, 16 males) recruited from a kindergarten in Cologne city centre participated in the study. Eligibility criteria for the participants were age (3–6 years) and ‘‘normal’’ BMI (15th–85th percentile, Kromeyer-Hauschild et al., 2001). In

2.2.2.1. Determination test for children (DTC). Processing speed was measured using the children’s version of the determination test. The reliability and validity of the determination test has been confirmed in previous studies (Baur et al., 2006; Sommer et al., 2010). The DTC assesses the individual’s accuracy and reaction speed

Please cite this article in press as: Mierau A et al. The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.03.008

A. Mierau et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

in situations requiring continuous, swift and varying responses to rapidly changing visual and acoustic stimuli for approximately 7–9 min (including instructions and practice period). The test uses five different coloured fish appearing on a pond as visual stimuli. The child reacts to the stimulus as fast as possible by pressing the button on the response panel corresponding to the colour of the fish (e.g. blue fish = blue button). In addition, there is an audio stimulus of a frog croaking in either a high or low tone. The corresponding buttons on the response panel are grey and black for high and low tones, respectively. All stimuli are presented adaptively (test form S1), meaning the speed of presentation adapts to the ability level of the child. The difficulty of the DTC arises from the need to sustain continuous, rapid and varying responses to rapidly changing stimuli. The level of difficulty depends primarily not on the stimulus–response pairing, but on the speed with which the stimuli change, as well as on the number of different stimuli and responses which the subject has to move between. The main test variable is ‘‘correct responses’’. In addition, the test yield scores for ‘‘median reaction time (median RT)’’, ‘‘incorrect responses’’ and ‘‘omitted responses’’. According to the manufacturer the internal consistency for the main variable (correct responses) ranges between a = 0.86 and a = 0.94 depending on age (Heidinger and Häusler, 2011). 2.2.2.2. Continuous visual recognition task (CVR). Visual working memory performance was measured using the ‘‘continuous visual recognition task’’ (CVR) via VTS (see above for details). The task was based on decisions requiring the participant to identify whether an item was presented new, or whether it had already been presented in the test. The lag between the first presentation of an item and its reappearance was varied systematically between 5 and 11 items. The configuration of the test used (S8) is a short version adapted for children. It consisted of an instruction, a practice period and the test period itself. Within the test period the items are presented one after each other, limited to just one item per screen page. The participants were required to decide for each item whether it was presented for the first (‘‘new’’ item) or for the second time (‘‘old’’ item), and to press a red or a green button, respectively. The item pool was made up of 108 pictures of concrete objects and other items that were difficult to describe verbally. After every entry of a response, the next item was presented automatically. The child was asked to work as fast and as accurately as possible. The participant’s answers in response to the items were divided into four different answer categories: (1) ‘‘old stimulus’’ – hit (green button), (2) ‘‘old stimulus’’ – incorrect negative (red button), (3) ‘‘new stimulus’’ – incorrect positive (green button), (4) ‘‘new stimulus’’ – correct rejection (red button). The main variables are number of hits (maximum 54), number of incorrect positives, and mean reaction time for hits (mean RT hits). The standardised and computer-assisted presentation of items results in a high objectivity for the test administration. The automated calculation of the test variables ensures high evaluation reliability. According to the manufacturer, reliability (Cronbach’s alpha) ranges between 0.78 and 0.86, depending on test form and sample (Kessler and Pietrzyk, 2003). 2.2.3. Sensorimotor performance Sensorimotor performance was assessed using the ‘‘KiMo’’ test. A detailed description of the test items, testing procedure and test quality criteria is provided by Klein et al. (2011). Briefly outlined, the ‘‘KiMo’’ is an age-appropriate sensorimotor screening method tailored to kindergarten children (3–6 years). It comprises the following five test items: (1) 4  4 m shuttle run (sec), (2) standing long jump (distance in cm), (3) 60 s one leg stand on a 4.5 cm wide and 38 cm long beam (number of ground contacts with the free leg, (4) sit and reach (distance of the fingertips to the level of the foot

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soles with values beyond the soles counted as positive centimetres) and (5) lateral jumping for 15 s (number of jumps). This compilation of items covers some the most relevant gross sensorimotor abilities (coordination, strength, speed and flexibility) of preschool children. In addition, we have added a longer distance run on an outdoor track in order to obtain an index of the children’s endurance capacity (anaerobic and aerobic fitness). The children were asked to run as fast as they could for 600 m, with time measured after 200 m as well as at the finishing line. The children started the run in two groups with twelve participants in each group. Children not running in the test, the accompanying parents and kindergarten stuff were positioned along the running track to cheer on the runners. 2.3. Procedure All measurements were conducted from 8 to 12 a.m. in the facilities of the kindergarten, except for the long distance run. Before testing, all examiners involved in data recording visited the children in the kindergarten during their daily routine on several occasions in order to ensure the children were familiar with the examiners. The order of tests was as follows: EEG, cognitive performance (DTC and CVR counterbalanced across subjects with 5 min break between tests) and KiMo (random introduction of test items). The long distance run (200 m/600 m) was conducted on a separate occasion within a maximum of two weeks after completion of the other tests. The parents were allowed to attend the test location, but were asked not to interact with their children during actual task performance. 2.4. Statistics Partial correlation analyses were calculated between the main variables with age as a control variable using the software package Statistica 7.1 (StatSoft, Tusla, USA). P-values  0.05 were considered statistically significant. Because of the explorative nature of the study P-values were not adjusted for multiplicity. 3. Results Descriptive statistics (means and standard deviations) of the cohort and the main variables are shown in Table 1. The participants’ mean age was 5.02 ± 0.67 years, and they had mean BMI of 15.13 ± 1.30 kg/m2, corresponding to the 25th–50th percentile (Kromeyer-Hauschild et al., 2001). The mean iAPF varied between 7.4 ± 0.99 Hz at frontal and 8 ± 1.06 Hz at occipital sites. The results for the partial correlation analyses with age as a control variable are presented in Table 2, and are described in the following subchapters. Each correlation was visually inspected and corrected for outliers/clusters if necessary. Fig. 1 shows typical examples of cases where clusters had substantial influence on the correlation and thus, were excluded from analyses. Overall, this procedure was applied in 60 out of 119 correlations. However, in most cases only one observation was removed. The highest number of removed observations was 6 (two times). 3.1. Correlations between sensorimotor and cognitive performance In line with our hypothesis, significant correlations were found between measures of sensorimotor and cognitive performance. Specifically, for the processing speed task (DTC) the number of lateral jumps within 15 s correlated negatively with the median RT and positively with the number of correct responses. In addition, the number of ground contacts with the free leg during the one legged stance correlated negatively with the number of correct

Please cite this article in press as: Mierau A et al. The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.03.008

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A. Mierau et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

Table 1 Descriptive statistics of the cohort and the main variables. Mean Participants (9 girls/16 boys)

Age (years) Height (cm) Weight (kg) BMI (kg/m2)

iAPF

Frontal (Hz) Central (Hz) Parietal (Hz) Temporal (Hz) Occipital (Hz)

Sensorimotor performance

Cognitive Performance

Lateral jumping (Nr. jumps) Sit and reach (cm) Standing long jump (cm) One leg stand (Nr. of ground contacts) Shuttle run (sec) 200 m run (sec) 600 m run (sec) DTC

CVR

SD

responses, and positively with the number of omitted responses. Finally, time needed to complete the 200 m run correlated negatively with the number of correct responses. For the visual working memory task (CVR), a significant negative correlation was found between the jumping distance and mean RT hits, as well as mean RT hits and time needed to complete the 200 m run. Furthermore, time needed to complete the 200 m and 600 m run correlated negatively with the number of hits.

5.02 109.61 19.35 15.13

0.67 6.24 3.25 1.30

7.40 7.55 7.47 7.87 8.01

0.99 1.19 1.09 0.95 1.06

21.00 4.04 77.90 15.60

7.53 5.14 25.57 9.28

Contrary to our prediction, no significant correlations were found between the iAPF and any of the cognitive performance measures.

11.13 72.25 292.88

2.29 18.09 88.52

3.3. Correlations between sensorimotor performance and iAPF

1.69 90.36 9.64 15.24 43.44 2.67 14.28

0.57 29.36 9.16 6.16 8.12 1.51 8.46

Median RT (sec) Nr. correct Nr. incorrect Nr. omitted Nr. hits Mean RT hits (sec) Nr. incorrect positive

BMI: body mass index, iAPF: individual alpha peak frequency, DTC: determination test for children, CVR: continuous visual recognition Task.

3.2. Correlations between cognitive performance and iAPF

In line with our hypothesis, significant correlations were found between measures of sensorimotor performance and children’s iAPF. Specifically, there was a significant negative correlation between the time needed to complete the shuttle run and iAPF at parietal and temporal sites. Furthermore, time needed to complete the 200 m and 600 m run also significantly correlated with frontal (600 m), central and parietal iAPF. In addition, there was a trend towards a statistically significant correlation between time needed to complete the 200 m run and the frontal (R = 0.4; P = 0.062) as well as the temporal (R = 0.4; P = 0.064) iAPF. As

Table 2 Age-corrected correlation coefficients (R) for the correlation between individual alpha peak frequency (iAPF) and sensorimotor performance, iAPF and cognitive performance, and sensorimotor and cognitive performance. Sensorimotor performance

iAPF

Frontal Central Parietal Occipital Temporal

Lateral jumping (N = 24)

Sit and reach (N = 24)

.0484 .0411 .0507 .0874 .1279

.0906 .1009 .1407 .1473 .2791

Standing long jump (N = 24) .1219 .1020 .1967 .2836 .3873

One leg stand (N = 24) .0918 .0080 .1859 .0140 .2609

Shuttle run (N = 24) .3336 .4011 .4642⁄ .2157 .5671⁄⁄

200 m run

600 m run

(N = 23)

(N = 23)

.4047 .5831⁄⁄ .5696⁄⁄ .0853 .4020

.5378⁄ .6845⁄⁄ .5809⁄⁄ .0369 .2012

Cognitive performance DTC

iAPF

Frontal Central Parietal Occipital Temporal

CVR

Median RT

Nr. correct

Nr. incorrect

Nr. omitted

Nr. hits

(N = 24)

(N = 24)

(N = 24)

(N = 24)

(N = 24)

Mean RT hits (N = 24)

.1685 .1426 .0268 .2368 .2529

.2701 .1116 .1636 .0817 .3377

.1168 .0489 .0318 .0443 .0788

.0703 .0632 .1027 .0372 .1730

.2148 .3805 .3544 .2039 .2192

.2464 .3466 .3067 .2154 .3539

One leg stand (N = 25)

Shuttle run (N = 25)

Nr. incorrect positive (N = 24) .2119 .0828 .2703 .1912 .2308

Sensorimotor performance Lateral jumping (N = 25) Cognitive performance

DTC

CVR

Median RT (sec) Nr. correct Nr. incorrect Nr. omitted Nr. hits Mean RT hits (sec) Nr. incorrect positive

Significant correlations are indicated by ⁄P < 0.05;

⁄⁄

.4411⁄ .4457⁄ .1995 .0543 .3656 .2092 .2842

P < 0.01;

Sit and reach (N = 25) .1762 .0224 .4010 .2605 .2406 .1108 .2034

Standing long jump (N = 25) .3238 .3960 .1526 .0237 .3438 .4631⁄ .1316

.0926 .4799⁄ .1781 .4061⁄ .0766 .1114 .3487

.0822 .2509 .1477 .3179 .0566 .0654 .2556

200 m run

600 m run

(N = 24)

(N = 24)

.2948 .4698⁄ .0132 .0656 .7011⁄⁄⁄ .4902⁄ .1785

.2830 .0211 .1317 .0747 .7210⁄⁄⁄ .1043 .0769

⁄⁄⁄

P < 0.001 (2-tailed).

Please cite this article in press as: Mierau A et al. The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.03.008

A. Mierau et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

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Fig. 1. Left: typical examples of correlations substantially biased by clusters/outliers (grey ellipse). Right: in case (A), exclusion of such clusters/outliers was associated with an increase of the correlation coefficient from initially non-significant to significant, whereas in case (B) the correlation coefficient decreased from significant to nonsignificant.

an example, the correlations between the 200 m run and iAPF are depicted in Fig. 2. 4. Discussion Previous research has established a close relationship between sensorimotor and cognitive development (Piek et al., 2008; Roebers and Kauer, 2009; Niederer et al., 2011). However, the neurobiological mechanisms and the directional nature of this relationship are not well understood and further research is required. The present study identified the interrelation between sensorimotor abilities, cognitive performance and iAPF, an EEG marker of global architectural and functional properties of the human brain, in healthy preschool children. To the best of our knowledge, this is the first study addressing the link between iAPF and behaviour in this age group. The mean iAPF of 8 Hz at occipital sites obtained in the present study is congruent with the expected iAPF for five year old children based on previous research (cf. Klimesch, 1999). We found positive correlations between selective sensorimotor abilities and iAPF; however, no significant correlations were observed between iAPF and cognitive performance. Specifically, locomotor skills correlated with iAPF across all cortical regions, except for the occipital cortex, with the strongest correlations found in the central and the parietal regions. This pattern of results is in agreement with previous findings on EEG during infancy and early childhood indicating the central rhythm is functionally dissociated from the occipital alpha rhythm (Marshall et al., 2002; Marshall and Meltzoff, 2011). In fact, Marshall et al. (2011) found the central 6–9 Hz rhythm in infants and young children is an analogue to the adult ‘‘mu’’ or ‘‘sensorimotor’’ rhythm which is well-known for its role in sensorimotor control (Kuhlman, 1978; Niedermeyer, 1997; Pfurtscheller and Lopes da Silva, 1999). The mu rhythm becomes prominent in the second year of life, a key period for the development of locomotor skills, and therefore, a relationship between

these two phenomena has been hypothesised for quite some time (cf. Marshall et al., 2002). However, this study is the first to provide empirical evidence for a close relationship between the peak frequency of the brain’s dominant oscillatory component at rest and locomotor skills (i.e. running) in young children. More recent studies indicate that the brain’s white matter architecture is a key determinant for the iAPF (Valdés-Hernández et al., 2010; Jann et al., 2012), and children’s aerobic fitness is associated with greater white matter integrity in the corpus callosum, corona radiata and superior longitudinal fasciculus (Chaddock-Heyman et al., 2014). Therefore, one could assume the observed relationship between children’s running performance and iAPF in this study may be mediated by aerobic fitness. However, significant correlations between iAPF and running performance were found for all running distances (4  4 m shuttle run, 200 m and 600 m), suggesting that the relationship between running performance and iAPF is probably mediated by improved neuromuscular coordination rather than aerobic fitness. In line with previous research, a close relationship was found between measures of sensorimotor and cognitive performance indicating children with improved sensorimotor abilities were faster and/or more accurate in cognitive task performance. However, these correlations were (diffusely) distributed among different variables and not limited to running performance. These results, combined with the absence of significant correlations between iAPF and cognitive performance, indicate the relationship between sensorimotor and cognitive performance is dissociated from the iAPF. Therefore, this relationship is likely to be mediated by more specific rather than global neural mechanisms. Furthermore, our results suggest that in contrast to adults, in children cognitive performance in tasks requiring speed of information processing and visual working memory is not related to the global architecture and functional properties of the brain, as indexed by the iAPF. This novel finding corresponds well with the results of Chatham et al. (2009) who observed that young children’s cognitive control

Please cite this article in press as: Mierau A et al. The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.03.008

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is ‘‘reactive’’ rather than ‘‘proactive’’ and thus, less guided by cognitive functions such as working memory. 4.1. Implications, limitations and future directions Together the results of this study confirm the existence of a close relationship between sensorimotor and cognitive development however, the two domains are differentially related to the iAPF. Specifically, we found that in young children the iAPF is related to locomotor skills, but not to cognitive performance in tasks requiring processing speed and visual working memory. The iAPF has generated considerable recent research interest. It is relatively easy to assess, and thus, has high potential for monitoring within-person changes of neurophysiological integrity from early childhood up to old age (Grandy et al., 2013b). However, although the iAPF has been repeatedly shown to relate to cognitive performance, its relationship to sensorimotor performance has not been investigated so far. Therefore, the findings of this study have important implications for the general concept of the iAPF as they highlight a close relationship between the iAPF and the sensorimotor domain (i.e. locomotor skills); although, for now this applies only for young children. Another important implication of the present study relates to the general understanding of the brain-behaviour relationship during childhood development. Given the iAPF is considered as a marker of the brain’s global functional architecture, our results suggest a close link between the development of sensorimotor skills, and locomotor skills in particular, and brain development. In contrast, cognitive performance in tasks requiring processing speed and visual working memory seems to be dissociated from the iAPF. The results of the present study should be interpreted with caution and evaluated within the context of its limitations. For example, our rationale for the selection of cognitive tasks was based upon adult studies consistently showing significant correlations between iAPF and speed of information processing as assessed by reaction times, as well as short-term memory performance (Klimesch, 1999). However, such ‘‘abstract’’ tests as DTC and CVR, although adapted to paediatric populations, may have little ecological validity (Isquith et al., 2013) as they do not reflect the requirements of interactive behaviour for adapting to the environment (Koziol and Lutz, 2013). Therefore, the results of the present study should be validated in a larger cohort of subjects with extended behavioural examination including ‘‘real-life’’ cognitive tests, as well as testing of procedural learning. The iAPF has been found to show considerable heritability (Posthuma et al., 2001; Smit et al., 2006) and stability with respect to cognitive interventions in healthy adults (Grandy et al., 2013b). On the other hand, Angelakis et al. (2007) demonstrated that the iAPF can be increased by neurofeedback training in older adults. Therefore, the nature and nurture of the observed relationship between iAPF and locomotor skills as well as the clinical significance of the iAPF with respect to neurodevelopmental disorders has yet to be elaborated in future studies. Acknowledgements We would like to thank all children, their parents and the stuff of the kindergarten for the participation in this study. We also wish to thank Kristin Manz for her help with ‘‘KiMo’’ data collection. Conflict of interest: none of the authors have potential conflicts of interest to be disclosed. Fig. 2. Correlations between time needed to complete the 200 m run and individual alpha peak frequency (iAPF) for the frontal, central, parietal, temporal and occipital region. R and P-values indicate age-corrected correlation coefficients and significance level (2-tailed), respectively.

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Please cite this article in press as: Mierau A et al. The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.03.008

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Please cite this article in press as: Mierau A et al. The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.03.008