No increased sensitivity in brain activity of adolescents exposed to mobile phone-like emissions

Clinical Neurophysiology 124 (2013) 1303–1308

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No increased sensitivity in brain activity of adolescents exposed to mobile phone-like emissions S.P. Loughran a,⇑, D.C. Benz a, M.R. Schmid a,b, M. Murbach c,d, N. Kuster c,d, P. Achermann a,b,e a

Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland c IT’IS Foundation, Zurich, Switzerland d Swiss Federal Institute of Technology (ETH), Zurich, Switzerland e Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland b

a r t i c l e

i n f o

Article history: Accepted 22 January 2013 Available online 18 February 2013 Keywords: Mobile phone Radiofrequency electromagnetic fields Children EEG Cognition

h i g h l i g h t s  The current study examined the potential sensitivity of adolescents to mobile phone-like electromagnetic field exposures.  Unlike previous studies conducted on adults, no significant effects of exposure were found.  Results suggest that contrary to popular belief, adolescents are not more sensitive to mobile phone emissions.

a b s t r a c t Objective: To examine the potential sensitivity of adolescents to radiofrequency electromagnetic field (RF EMF) exposures, such as those emitted by mobile phones. Methods: In a double-blind, randomized, crossover design, 22 adolescents aged 11–13 years (12 males) underwent three experimental sessions in which they were exposed to mobile phone-like RF EMF signals at two different intensities, and a sham session. During exposure cognitive tasks were performed and waking EEG was recorded at three time-points subsequent to exposure (0, 30 and 60 min). Results: No clear significant effects of RF EMF exposure were found on the waking EEG or cognitive performance. Conclusions: Overall, the current study was unable to demonstrate exposure-related effects previously observed on the waking EEG in adults, and also provides further support for a lack of an influence of mobile phone-like exposure on cognitive performance. Significance: Adolescents do not appear to be more sensitive than adults to mobile phone RF EMF emissions. Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction There has been increasing interest in recent years regarding whether radiofrequency electromagnetic fields (RF EMF), such as those emitted by mobile phones, have an influence on brain activity. Previous research has indeed shown that pulse-modulated RF EMFs characteristic of those emitted by mobile phones affect the electroencephalogram (EEG) during both sleep and waking in adults (e.g. Borbély et al., 1999; Croft et al., 2002; Curcio et al., 2005; Huber et al., 2000; Loughran et al., 2005; Regel et al., 2007a; Reiser et al., 1995; van Rongen et al., 2009). In particular, ⇑ Corresponding author. Address: Institute of Pharmacology and Toxicology, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. Tel.: +41 44 63 559 54; fax: +41 44 63 557 07. E-mail address: [email protected] (S.P. Loughran).

the alpha frequency range during waking and the alpha and spindle frequency ranges during sleep have been the most commonly reported areas influenced by such exposures to RF EMF. However, despite this increasing evidence of a repeatable mobile phone-induced effect on brain activity in adults, very little research exists regarding the presence and/or magnitude of this effect in children and adolescents. Mobile phones are a dominant component of modern telecommunications technology and constitute the main source of RF EMF exposure for children and adolescents. In 2006 the World Health Organization (WHO) released a research agenda specifically relating to RF EMF in which investigations on potential effects on the EEG and cognition in children were identified as a high priority research need. A subsequent research agenda from the WHO stated that there have only been few such studies since this initial

1388-2457/$36.00 Ó 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinph.2013.01.010

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recommendation and therefore highlighted that further RF EMF provocation studies on children of different ages were still required (WHO, 2010). In addition, it was also suggested that neurobiological mechanisms and possible thresholds and dose–response relationships should also be investigated. During normal mobile phone use, i.e., when operated at the head, some of the emitted RF EMF penetrates into the head tissues where it is absorbed. The distribution of the induced fields depends on the design of the phone, its position at the head and the brain anatomy (tissue distribution and dielectric parameters). Christ et al. (2010a) showed that local maximum averaged absorption is similar between children and adults but that the locally induced fields in certain subregions of the children’s brain (specifically the cortex, hippocampus and hypothalamus) can be significantly higher (on average by about a factor of two) compared to adults due to anatomical reasons. In general, the maximum specific absorption rate (SAR) of the brain tissues of adults and children is considerably below the basic restrictions of 2 W/kg (by more than a factor of two for children and a factor of four for adults). These restrictions were proposed by the International Commission on Non-Ionizing Radiation Protection (1998) as safety limits for the general population and have since been adopted by most countries. However, despite these restrictions it remains that effects on brain physiology still occur in adults at exposures well below the currently accepted safety guidelines (for review, see van Rongen et al., 2009). In regards to the possibility of effects on children and adolescents this becomes especially important for several reasons. Children start to use mobile phones extensively in early adolescence and they might be particularly sensitive to RF EMF as maturational cortical changes are ongoing throughout development (Segalowitz et al., 2010; Whitford et al., 2007). In view of the higher brain exposure of children, it surprising that very few studies have investigated in younger cohorts the influence observed on the EEG in adults, or even the inconsistent effects reported on cognitive performance (for review, see van Rongen et al., 2009). Therefore, the current experiment aimed to determine whether RF EMF exposure also influences the waking EEG and/or cognitive performance in adolescents and to establish a possible dose–response relationship. 2. Materials and methods 2.1. Participants Twenty-two young, healthy, right-handed adolescents (12 males) aged 11–13 years (mean age 12.3 ± 0.8 years) participated in this experiment. Pubertal status was assessed and determined as reported by the parents using the standardized Tanner staging system (adapted from Carskadon and Acebo, 1993), and only one female in the sample had reached menarche. Participants with a history of neurologic or psychiatric disorders were excluded from the study. Additionally, all subjects were medication and drug free at the time of participation. Participants were recruited mainly through schools, broadcasts on national television, and advertisements in local newspapers. All study protocols were approved by the Cantonal Ethical Committee for research on human participants and written informed consent was obtained from the participants’ legal guardian prior to participation. The subjects received a cinema, book, or CD voucher and a t-shirt as compensation for their participation. 2.2. Study procedure The study was carried out at the sleep laboratory of the Institute of Pharmacology and Toxicology, University of Zurich. In a double-

blind, randomized, and counter-balanced crossover design, participants underwent three different exposure conditions at weekly intervals. Each session was performed at the same time of day within participants. During the study participants were required to abstain from caffeine and had to adhere to regular bed-times starting three days before each study day. Compliance for prior sleep activity was controlled by wrist-worn actimeters and sleep logs. Additionally, on all study days physical exercises had to be avoided and the use of mobile phones for calling was prohibited. At each session, electrodes were first applied and then a baseline waking EEG (3 min eyes close, 3 min eyes open) was recorded prior to each of the three exposure conditions. During the exposure participants sat with their heads positioned between the two exposure antennas. Each exposure lasted 30 min, during which cognitive tasks were performed. The waking EEG (3 min of eyes closed followed by 3 min of eyes open) was then recorded in a different room (in the same manner and location as the baseline EEG recordings) immediately after exposure, and again at 30 min and 60 min after exposure in order to be comparable to a previous study conducted at our laboratory on adults (Regel et al., 2007a). At the conclusion of each experimental session participants were asked whether they were able to perceive the field. In addition, 100-mm visual analog scales were administered at each session prior to the EEG recording. Subjects were asked to rate themselves on the following items with the anchors indicated in parenthesis: tiredness (0 mm = tired; 100 mm = alert), general mood (0 mm = good mood; 100 mm = bad mood), energy (0 mm = lethargic; 100 mm = energetic), tension (0 mm = relaxed; 100 mm = stressed) and concentration (0 mm = concentrated; 100 mm = unable to concentrate). 2.3. Exposure conditions The three exposure conditions were applied via a planar antenna at the left side of the participants head in order to be consistent with our previous studies (e.g. Huber et al., 2002; Schmid et al., 2012). Dosimetric assessment revealed a 9% assessment uncertainty and 15% inter-subject variation (both values denote one standard deviation, SD) for the guideline relevant psSAR values. To ensure compliance with the ICNIRP limit of 2 W/kg, the higher intensity condition was lowered by 30% (2 times SD of inter-subject variation). Therefore the 3 exposure conditions were: (1) GSM handset-like modulation, 900 MHz carrier frequency, peak spatial SAR (psSAR) 1.4 W/kg (‘high SAR’); (2) GSM handset-like modulation, 900 MHz carrier frequency, psSAR 0.35 W/kg (‘low SAR’); and (3) Sham (no field). A refined analysis with four different children models revealed actual mean exposure levels of 1.33 W/kg (±13%) for the targeted 1.4 W/kg. During exposure, the participant was seated comfortably between two planar antennas (left active only) in order to ensure a well-defined exposure and constant positioning (for details, see Boutry et al., 2008; Huber et al., 2002, 2005; Murbach et al., 2012; Schmid et al., 2012). Additionally, in order to eliminate or minimize any potential interference with the applied RF EMF signals, the attached electrode leads were oriented horizontally to the emitted field. Comparison of the exposure distribution between adults and children was also performed, and details shown in Table 1. 2.4. EEG data acquisition At each time point waking EEG data (C3LM, C4LM, O1LM and O2LM derivations; LM = linked mastoid) were recorded, as well as electrooculogram (EOG) and electrocardiogram (ECG) using a polygraphic amplifier Artisan (Micromed, Mogliano Veneto, Italy). The analog signals were high-pass filtered (EEG: 3 dB at 0.15 Hz; ECG: 1 Hz) and low-pass filtered ( 3 dB at 67.2 Hz), sampled at

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Table 1 Simulated average exposure and variation (1 SD) for three adults models (Ella f/26y, Duke m/34y, HR female) and four children (Billie f/11y, Eartha f/8y, Louis m/14y, Thelonius m/6y) with the patch antenna configuration (Christ et al., 2010b). The thalamus in children shows a higher exposure, as anticipated, due to generally smaller head dimensions. Other deviations are within expected inter-subject variations of adult head anatomies (Murbach et al., 2012). Adults mean (variation) W/kg

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Difference (%)

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1.23 (±15%) 1.42 (±12%) 0.26 (±35%)

1.33 (±13%) 1.62 (±6%) 0.38 (±22%)

+8 +14 +46

<9 <5 <16

During exposure participants performed three different cognitive tasks (‘‘Simple Reaction Time Task’’ (SRT), ‘‘2-Choice Reaction Time Task’’ (CRT), and the ‘‘N-Back Task’’). To assess possible changes that might occur during exposure, each task was presented twice in a fixed order (SRT, CRT, 1-, 2-back versions of the N-Back task). Between each session, and at the end of the second session, there was an approximate break of 5 min in which participants watched a short silent movie while exposure continued. The cognitive tasks were applied and analyzed as described by Regel et al. (2007b). In brief, for the SRT participants were required to press a ‘0’ on the response box with their right index finger whenever a ‘0’ appeared on the screen. For the CRT, participants were required to press either a ‘J’ or ‘N’ button (right index and middle fingers) on the response box whenever ‘JA’ (yes) or ‘NEIN’ (no) appeared on the screen. Only the first two levels of the N-Back task were included in the test battery as the third level of the N-Back was deemed to be too difficult for the age range of the participants included in the current study based on the results of a pilot study we performed to assess task appropriateness. The two levels used consisted of a one-by-one random sequence of consonants, and participants were instructed to compare the current letter shown on the screen with the letter that had appeared either one (1-Back) or two (2-Back) trials back and respond by pressing ‘J’ for letters that were the same or ‘N’ for letters that were different. 2.6. Data analysis 2.6.1. Electroencephalogram EEG data was visually inspected for artifacts and then segmented into two-second epochs (frequency resolution = 0.5 Hz). Spectral analysis was performed on artifact free epochs (Hanning window, 50% overlap) in the eyes closed condition. Due to high inter-individual variation in the position and size of the alpha peak as well as variation of peak size over time, relative spectra were calculated. For each participant, the position of the alpha-peak frequency was determined in the baseline spectra of the three experimental conditions (high SAR, low SAR, and sham). Spectra at 0, 30, and 60 min after exposure were centered at the alpha peak frequency of the corresponding baseline and expressed relative to this baseline. Linear mixed models (presuming an identical intraclass correlation for all participants) were used for analysis of EEG spectral power (SAS 9.1.3; SAS Institute Inc., Cary, NC, USA) and

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256 Hz, and recorded using Rembrandt DataLab (Version 8.0; Embla Systems, Broomfield, CO, USA). During the EEG recordings participants were seated comfortably on a chair with their head resting on a chin rest. In order to minimize eye movements and for consistency with previous studies from our laboratory (e.g. Regel et al., 2007a), participants were instructed to lightly place their index fingers on their eyelids during the eyes closed condition and to fixate on a black dot that was positioned in front of them at eye-height in the eyes open condition. Recordings were continuously monitored online in order to ensure vigilance was maintained.

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Frequency [Hz] Fig. 1. RF EMF induced changes in the power spectra of the waking EEG (derivation O1LM, eyes closed; n = 20) 0, 30 and 60 min following exposure. Power spectra were centered ±5 Hz around the alpha peak frequency in the corresponding baseline (average 9.64 Hz). Spectra in each condition were first expressed relative to the corresponding baseline and, subsequently, relative to the sham condition (100%). Relative power spectra (geometric mean) (a) low SAR vs. sham; (b) high SAR vs. sham; (c) F-values of linear mixed-model ANOVA for the factor ‘condition’. Significant values (p < 0.05) are indicated by black bars. No interactions of factors ‘condition’ and ‘time’ (0, 30, 60 min after exposure) were observed. Effects of ‘condition’ were post hoc evaluated with two-tailed paired t-tests and the significant change observed immediately (0 min) after exposure indicated with a black triangle. RF EMF, radio frequency electromagnetic fields.

included the factors Condition (‘High SAR’, ‘Low SAR’ and sham), Order of Sham (1, 2, 3), Time (0, 30, 60 min after exposure), and their interactions. Frequency ranges were only considered to be meaningful if at least two consecutive frequency bins reached significance (i.e., a range of 1 Hz), which aimed to ensure that any effects were physiologically meaningful while also compensating for any issues related to multiple comparisons. Post-hoc analyses comprised two-tailed paired t-tests. Two participants had to be excluded from the EEG analysis, one due to high frequency noise and one due to a corrupted baseline recording.

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3.2. Cognitive tasks and behavioral measures

2.6.2. Cognitive tasks Cognitive tasks were analyzed as per several of our previous studies and full details can be found elsewhere (Regel et al., 2007a). Statistical analyses were carried out using linear mixed model ANOVAs including the factors Condition (High SAR, Low SAR, sham), Session (1, 2), Order of Sham (1, 2, 3), and associated interactions. Accuracy of performance was not normally distributed and therefore non-parametric Wilcoxon Signed Rank tests were performed.

No significant differences between exposure conditions were observed for any of the three cognitive tasks. This was the case for both reaction speed and accuracy, and is shown in Fig. 2. Regarding behavioral measures across weeks, a repeated measures ANOVA revealed a significant change in mental tension (F = 4.248; p = 0.036). A post hoc paired t-test revealed that there was a significant decrease in mental tension between weeks one and three (t(df) = 2.37; p = 0.028) but this was not present between weeks one and two (t(df) = 1.88; p = 0.075). In addition, the field perception questionnaire showed that participants were unable to detect the status of exposure.

3. Results 3.1. Waking EEG

4. Discussion

Spectral analysis of the waking EEG revealed significant main effects of ‘condition’ (p < 0.05) at 6 Hz and between 12 and 13.5 Hz. Post-hoc paired t-tests revealed higher power in the 12 Hz frequency bin following the low SAR exposure condition immediately (0 min) after exposure (p < 0.05). Data for the left occipital derivation are shown in Fig. 1. No further significant differences between the exposure conditions were observed and the relative EEG spectra were similar for both hemispheres. Furthermore, no consistent effects of time (i.e., 0, 30, or 60 min after exposure) were observed.

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The present study was designed to further extend previously reported effects of RF EMF on healthy adults by looking at potential effects in younger subjects, and determine whether adolescents are indeed more sensitive to such exposures. For brain exposures higher than the maximum exposure during normal usage of a mobile phone (i.e., 1.4 W/kg versus <1 W/kg), we were unable to demonstrate any exposure related effects on the waking EEG or cognitive performance. These results not only suggest that adolescents are 2-Back

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Session Fig. 2. Effect of RF EMF exposure on cognitive performance (n = 22). The three exposure conditions applied were high SAR (high), low SAR (low) and Sham. (a) Reaction speeds for the SRT, CRT, and N-Back tasks separated by session (session 1 = first half of exposure, session 2 = second half of exposure). (b) Accuracy of performance for the SRT, CRT, and N-Back tasks separated by session. A linear mixed model ANOVA revealed no significant differences between exposure conditions for either reaction speed or accuracy of performance.

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not more sensitive to mobile phone-like RF EMF exposures, but also consistent with another recent study, demonstrate that effects on the EEG, if present, are less prominent than those observed in adults (Croft et al., 2010). It has been shown several times previously that the adult waking EEG is influenced in the alpha frequency range by pulse-modulated RF EMF (Croft et al., 2002, 2008, 2010; Curcio et al., 2005; Huber et al., 2002; Regel et al., 2007a; Reiser et al., 1995), and in further support of exposure related effects on the EEG, the influence on the EEG during sleep in the alpha and spindle frequency ranges is arguably the most consistent effect reported to date (Borbély et al., 1999; Huber et al., 2000, 2002, 2005; Loughran et al., 2005, 2012; Regel et al., 2007b; Schmid et al., 2012). Despite this, the current study was unable to demonstrate a similar effect on the waking EEG in adolescents, although there was a suggestion of an influence of activity around 12 Hz in the low intensity exposure condition. This was not considered as a reliable effect on the EEG because our methodological criteria required changes to be observed in at least two adjacent frequency bins to be considered significant. There are several potential reasons why a lack of a clear influence on the EEG was observed in the current study, most notably the influence of high individual variability as highlighted by a number of recent studies (Croft et al., 2010; Leung et al., 2011; Loughran et al., 2012; Schmid et al., 2012). Indeed, regarding the sleep EEG, it was recently shown that individual variability may play a large but important role in the effects observed in RF bioeffects research, suggesting that negative results regarding an influence on the EEG may not be strong evidence for a lack of effects from exposure to RF EMF (Loughran et al., 2012). The presence of individual variability may be even more problematic in studies investigating exposure-related effects on children and adolescents, such as the current study, as the variability across development is even greater (Segalowitz et al., 2010). Small or subtle effects on the EEG, as have been previously reported in adults, would have been particularly difficult to detect without the use of an extremely large sample or a more homogenous group developmentally and therefore raises the possibility that the current study may have been underpowered to detect such an effect. Another potential reason for the lack of an exposure-related effect on the EEG could be due to the timing of the effect, or that there exists a particular time window in which the effect would occur and be seen. For example, the effect may be short-lived and have occurred between EEG recordings performed in the current study, or alternatively, could take longer to manifest (as has been reported in some studies investigating effects on the sleep EEG) and therefore may not have been seen until more than 60 min after the cessation of exposure. On the contrary, it could also be that pulse-modulated RF EMF does not significantly affect the EEG of adolescents, and that this effect is specific to adults. In support of this, adolescent’s brains are more plastic and therefore may be able to adapt more readily to small stressors or external influences such as mobile phone-like exposures. This would also be supported by a recent study which found an influence of pulse-modulated RF EMF on the EEG of healthy young adults (aged 19–40 years) but not adolescents (aged 13–15 years), in which the authors interpreted the results as suggesting that the adolescent brain may be more robust to subtle mobile phone-related effects (Croft et al., 2010). In regards to cognition, the current study provides further support to several previous studies suggesting that cognitive performance is not influenced by pulse-modulated RF EMF exposures. Indeed recent reviews mostly regarding studies performed in adults concluded that currently there is no clear evidence that cognition is influenced by exposure to mobile phone-type exposures (Regel and Achermann, 2011; Valentini et al., 2010; van Rongen et al., 2009; Kwon and Hamalainen, 2011). However, it should also

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be noted that a recent well-performed study comparing effects of exposure on three different age groups (adolescents, young adults, and the elderly) did find exposure-related effects on cognitive performance, and in particular showed that adolescents performed worse on the N-Back task during a 3G-type exposure (Leung et al., 2011). The difference between the study by Leung et al. (2011) and both the current and all previous studies is that cognitive tasks were tailored to each individuals ability level. This suggests that, as for the EEG, individual variability may play a big role in exposure-related effects, meaning that it would have been unlikely for subtle effects to be detected in the current study given the sample size and age of participants, and indeed in many previous studies conducted in adults. In conclusion, the current study was unable to demonstrate any exposure-related effects on the EEG or cognitive performance in adolescents. Importantly, the results also do not provide evidence for an increased sensitivity to RF EMF in adolescents. Furthermore, the idea of an age-specific effect is of particular interest given the increasing use of mobile phone technology across all ages, and therefore it would be particularly important for further research to explore children at other stages of development. Acknowledgements The authors thank Sarah Münst and Iva Jelezarova for their assistance in the sleep laboratory, and Karl Wüthrich and Dr. Roland Dürr for technical support. This study was supported by the Swiss National Science Foundation (National Research Programme 57: ‘Non-Ionizing Radiation – Health and Environment’). References Borbély AA, Huber R, Graf T, Fuchs B, Gallmann E, Achermann P. Pulsed highfrequency electromagnetic field affects human sleep and sleep electroencephalogram. Neurosci Lett 1999;275:207–10. Boutry CM, Kuehn S, Achermann P, Romann A, Keshvari J, Kuster N. Dosimetric evaluation and comparison of different RF exposure apparatuses used in human volunteer studies. Bioelectromagnetics 2008;29:11–9. Carskadon MA, Acebo C. A self-administered rating scale for pubertal development. J Adolesc Health 1993;14:190–5. Christ A, Gosselin MC, Christopoulou M, Kuhn S, Kuster N. Age-dependent tissuespecific exposure of cell phone users. Phys Med Biol 2010a;55:1767–83. Christ A, Kainz W, Hahn EG, Honegger K, Zefferer M, Neufeld E, et al. The Virtual Family – development of surface-based anatomical models of two adults and two children for dosimetric simulations. Phys Med Biol 2010b;55:N23–38. Croft RJ, Chandler JS, Burgess AP, Barry RJ, Williams JD, Clarke AR. Acute mobile phone operation affects neural function in humans. Clin Neurophysiol 2002;113:1623–32. Croft RJ, Hamblin DL, Spong J, Wood AW, McKenzie RJ, Stough C. The effect of mobile phone electromagnetic fields on the alpha rhythm of human electroencephalogram. Bioelectromagnetics 2008;29:1–10. Croft RJ, Leung S, McKenzie RJ, Loughran SP, Iskra S, Hamblin DL, et al. Effects of 2G and 3G mobile phones on human alpha rhythms: resting EEG in adolescents, young adults, and the elderly. Bioelectromagnetics 2010;31:434–44. Curcio G, Ferrara M, Moroni F, D’Inzeo G, Bertini M, De Gennaro L. Is the brain influenced by a phone call? An EEG study of resting wakefulness. Neurosci Res 2005;53:265–70. Huber R, Graf T, Cote KA, Wittmann L, Gallmann E, Matter D, et al. Exposure to pulsed high-frequency electromagnetic field during waking affects human sleep EEG. Neuroreport 2000;11:3321–5. Huber R, Treyer V, Borbély AA, Schuderer J, Gottselig JM, Landolt HP, et al. Electromagnetic fields, such as those from mobile phones, alter regional cerebral blood flow and sleep and waking EEG. J Sleep Res 2002;11:289–95. Huber R, Treyer V, Schuderer J, Berthold T, Buck A, Kuster N, et al. Exposure to pulsemodulated radio frequency electromagnetic fields affects regional cerebral blood flow. Eur J Neurosci 2005;21:1000–6. International Commission on Non-Ionizing Radiation Protection. Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz). Health Phys 1998;74:494–522. Kwon MS, Hamalainen H. Effects of mobile phone electromagnetic fields: critical evaluation of behavioral and neurophysiological studies. Bioelectromagnetics 2011;32:253–72. Leung S, Croft RJ, McKenzie RJ, Iskra S, Silber B, Cooper NR, et al. Effects of 2G and 3G mobile phones on performance and electrophysiology in adolescents, young adults and older adults. Clin Neurophysiol 2011;122:2203–16.

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