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Optics Express

Optics Express

  • Editor: Michael Duncan
  • Vol. 14, Iss. 13 — Jun. 26, 2006
  • pp: 6128–6141
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Do GSM 900MHz signals affect cerebral blood circulation? A near-infrared spectrophotometry study

Martin Wolf, Daniel Haensse, Geert Morren, and Juerg Froehlich  »View Author Affiliations


Optics Express, Vol. 14, Issue 13, pp. 6128-6141 (2006)
http://dx.doi.org/10.1364/OE.14.006128


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Abstract

Effects of GSM 900MHz signals (EMF) typical for a handheld mobile phone on the cerebral blood circulation were investigated using near-infrared spectrophotometry (NIRS) in a three armed (12W/kg, 1.2W/kg, sham), double blind, randomized crossover trial in 16 healthy volunteers. During exposure we observed borderline significant short term responses of oxyhemoglobin and deoxyhemoglobin concentration, which correspond to a decrease of cerebral blood flow and volume and were smaller than regular physiological changes. Due to the relatively high number of statistical tests, these responses may be spurious and require further studies. There was no detectable dose-response relation or long term response within 20min. The detection limit was a fraction of the regular physiological changes elicited by functional activation. Compared to previous studies using PET, NIRS provides a much higher time resolution, which allowed investigating the short term effects efficiently, noninvasively, without the use of radioactive tracers and with high sensitivity.

© 2006 Optical Society of America

1. Introduction

2. Methods

2.1. Near-Infrared Spectrophotometry

The principle of NIRS is described extensively in [13

13. D. Haensse, P. Szabo, D. Brown, J. C. Fauchere, P. Niederer, H. U. Bucher, and M. Wolf, “New multichannel near infrared spectrophotometry system for functional studies of the brain in adults and neonates,” Opt. Express 13, 4525–4538 (2005). [CrossRef] [PubMed]

, 14

14. S. Wray, M. Cope, D. T. Delpy, J. S. Wyatt, and E. O. Reynolds, “Characterization of the near infrared absorption spectra of cytochrome aa3 and haemoglobin for the non-invasive monitoring of cerebral oxygenation,” Biochim. Biophys. Acta 933, 184–192 (1988). [CrossRef] [PubMed]

]. In short, a near-infrared light source is placed on the head. At a certain distance a detector measures the intensity of the light, which re-emerges from the tissue. It has been shown that the photons, which travel from the source to the detector carry information about the brain [11

11. V. Toronov, A. Webb, J. H. Choi, M. Wolf, A. Michalos, E. Gratton, and D. Hueber, “Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging,” Med. Phys. 28, 521–527 (2001). [CrossRef] [PubMed]

]. Thus from the changes in light intensity, information about the changes in the blood circulation of the brain can be obtained.

Tissue can be optically characterized by two parameters: scattering and absorption. Theoretical models for light transport through tissue have been derived, mainly by using the diffusion approximation to the Boltzmann transport equation for the semi-infinite boundary condition. Using this approximation, mathematical models have been derived and tested to analyze the optical data and quantify O2Hb and HHb concentration changes [14

14. S. Wray, M. Cope, D. T. Delpy, J. S. Wyatt, and E. O. Reynolds, “Characterization of the near infrared absorption spectra of cytochrome aa3 and haemoglobin for the non-invasive monitoring of cerebral oxygenation,” Biochim. Biophys. Acta 933, 184–192 (1988). [CrossRef] [PubMed]

16

16. A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39, 889–894 (1996). [CrossRef] [PubMed]

]. These algorithms are now well established.

For the detection of changes in CBF and CBV, it is sufficient to quantify changes in O2Hb- and HHb- concentration. The sum of O2Hb and HHb equals to the total hemoglobin concentration (tHb), which is proportional to the CBV and can be used to calculate the CBV. A change in CBV leads to a parallel change in both O2Hb and HHb [10

10. M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi, and E. Gratton, “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study,” Neuroimage 16, 704–712 (2002). [CrossRef] [PubMed]

]. In contrast a change in CBF leads to a parallel change in O2Hb and an opposite change in HHb [10

10. M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi, and E. Gratton, “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study,” Neuroimage 16, 704–712 (2002). [CrossRef] [PubMed]

]. In the rest of this paper we will consider the primary NIRS parameters O2Hb and HHb.

For imaging applications a mesh of sources and detectors is placed on the region of interest. The measured data provides spatially resolved O2Hb- and HHb- concentrations.

For this study the High Speed Optical Brain Imager MCP II, developed in our laboratory and described in [13

13. D. Haensse, P. Szabo, D. Brown, J. C. Fauchere, P. Niederer, H. U. Bucher, and M. Wolf, “New multichannel near infrared spectrophotometry system for functional studies of the brain in adults and neonates,” Opt. Express 13, 4525–4538 (2005). [CrossRef] [PubMed]

] was used. The instrument is equipped with light emitting diodes (LEDs) for two wavelengths at 730nm and 830nm. It provides a data acquisition rate of 100Hz and a resolution of 16bit. Two imaging sensors allow covering an area of twice 2.5cm×3.75cm with 16 channels each and produce 2D images of the changes in O2Hb and HHb.

Fig. 1. The configuration of the sensor. Purple quadrangles indicate source positions with LEDs and blue squares indicate PIN photodiode detectors.

A trigger signal can be directly read through an analog input. This was used to record the EMF on and off status during the experiment.

2.2. Exposure System

The same exposure system as in [2

2. R. Huber, T. Graf, K. A. Cote, L. Wittmann, E. Gallmann, D. Matter, J. Schuderer, N. Kuster, A. A. Borbely, and P. Achermann, “Exposure to pulsed high-frequency electromagnetic field during waking affects human sleep EEG,” Neuroreport 11, 3321–3325 (2000). [CrossRef] [PubMed]

4

4. R. Huber, J. Schuderer, T. Graf, K. Jutz, A. A. Borbely, N. Kuster, and P. Achermann, “Radio frequency electromagnetic field exposure in humans: Estimation of SAR distribution in the brain, effects on sleep and heart rate,” Bioelectromagnetics 24, 262–276 (2003). [CrossRef] [PubMed]

] was used. The exposure system has been extensively characterized including the dosimetry, e.g. the dose within different head tissues dependent on the input power of the antenna [4

4. R. Huber, J. Schuderer, T. Graf, K. Jutz, A. A. Borbely, N. Kuster, and P. Achermann, “Radio frequency electromagnetic field exposure in humans: Estimation of SAR distribution in the brain, effects on sleep and heart rate,” Bioelectromagnetics 24, 262–276 (2003). [CrossRef] [PubMed]

]. The same pulse-modulated, “handset-like” GSM signal (900MHz carrier) as in [2

2. R. Huber, T. Graf, K. A. Cote, L. Wittmann, E. Gallmann, D. Matter, J. Schuderer, N. Kuster, A. A. Borbely, and P. Achermann, “Exposure to pulsed high-frequency electromagnetic field during waking affects human sleep EEG,” Neuroreport 11, 3321–3325 (2000). [CrossRef] [PubMed]

4

4. R. Huber, J. Schuderer, T. Graf, K. Jutz, A. A. Borbely, N. Kuster, and P. Achermann, “Radio frequency electromagnetic field exposure in humans: Estimation of SAR distribution in the brain, effects on sleep and heart rate,” Bioelectromagnetics 24, 262–276 (2003). [CrossRef] [PubMed]

] at two different power levels (average pulse peak power of 12 and 1.2W/kg spatial peak specific absorption rate (SAR) averaged over 10g of tissue) was applied. For the chosen exposure protocol this results in an averaged spatial peak SAR of 1.5W/kg for the highest power level over the course of the experiment. The study room was particularly suitable for these measurements, because it provided a constant ambient temperature and silence. Ambient magnetic fields measured by a Wandel & Goltermann EM Filed Analyzer EFA3 between 5Hz and 30kHz were 53.4±5.2nT and thus neglectable.

2.3. Electromagnetic interference

In a test prior to this study the exposure system generating a defined SAR distribution in the head was operated together with the NIRS instrument to warrant electromagnetic compatibility of all the equipment involved. The NIRS equipment was mounted on an experimental phantom used for compliance testing of mobile phones filled with tissue simulating liquid. This phantom also had similar optical properties as the human head and thus was appropriate to assess the influence of EMF on the NIRS measurement and vice versa. The antenna used for exciting the EMF was positioned according to the experimental setup.

The signal of the NIRS instrument was recorded for different positions of the sensor at the head, including the ones used below, during on/off cycles of the radiation from the antenna. The EMF lead to electromagnetic interference (EMI) on the NIRS signal, which increased linearly proportional to the power of the EMF. The size of the interference was also dependent on the location. However, the effects on the NIRS-signals were synchronous to the EMI, i.e. they disappeared immediately once the EMF was turned off. To obtain NIRS-signals without the influence of EMI, the protocol provides intermittent periods without EMF.

For the same configurations with and without NIRS equipment the distribution of the SAR was measured and compared. The influence of the sensor on the SAR distribution in the head can be significant depending on the orientation of the connecting cable. Therefore, by using an appropriate positioning of the cable these effects can be reduced. The sensor causes slightly higher maximum spatial peak SAR values in the head. However, this deviation is <0.5dB for our configurations and is well below the limits required by current safety guidelines.

These steps ensured the safety of the volunteers participating within the studies and the correct function of all equipment involved.

2.4. Study protocol

Based on the previously found effects of EMF on cerebral blood flow, we defined the following four hypotheses that were to be tested by our study:

1. There is a short term response of O2Hb or/and HHb to EMF within 20s during exposure.

2. There is a short term response of O2Hb or/and HHb to EMF within 40s after exposure.

3. There is a long term response of O2Hb or/and HHb to EMF, which occurs within 20min.

4. There is a dose-response relation, i.e. the change in O2Hb or/and HHb is higher:

a. When exposed to a higher EMF dose.

b. When comparing the two sides of the head, one towards, one opposite to the antenna.

The study was designed as a three armed, double blind, randomized crossover trial. Each subject underwent three exposures (spatial peak SAR 12W/kg, 1.2W/kg, sham exposure) in randomized order on three different days, making measurements at the same time of day for each subject. Randomization was achieved by computer. All displays, which would indicate the type of exposure, were covered for the whole period of measurement, such that neither the operator nor the subject had any indication of the type of exposure. Thus, a double blind measurement environment was secured.

Fig. 2. Diagram of the experimental set-up. The sensors were placed above area B (MNI coordinates: x=-56, y=10, z=20) on the left hemisphere and the respective position on the other hemisphere

The subjects were asked to refrain from caffeine, nicotine, and cell phone use for 2 hours prior to the measurement. Prior to the measurements we obtained the following information from the subjects: age, handedness, sleep quality, tiredness, and degree of consumption of caffeine, nicotine and cell phone use. We also noted room and outside temperatures. During the experiment subjects were seated on a chair, asked to remain completely still and to count backwards from 2000 in order to keep the prefrontal cortex in steady activity. The antenna was positioned on a location close to the left ear, typical for a handheld mobile phone (Fig. 2). One NIRS-sensor was placed on area B of the prefrontal cortex identified in a PET-study, where an increase in CBF was measured [3

3. R. Huber, V. Treyer, A. A. Borbely, J. Schuderer, J. M. Gottselig, H. P. Landolt, E. Werth, T. Berthold, N. Kuster, A. Buck, and P. Achermann, “Electromagnetic fields, such as those from mobile phones, alter regional cerebral blood flow and sleep and waking EEG,” J. Sleep. Res. 11, 289–295 (2002). [CrossRef] [PubMed]

]. The other sensor was placed on the same area at the right hemisphere.

In a pilot study we found that shortly after placing the sensors on the head, the O2Hb and HHb concentration changed. Possible reasons for this are described in detail in the discussion section. Since the strongest changes occur within the first two minutes, we introduced a 4 minute baseline measurement prior to the main trial period. The baseline period allowed parameters to settle after the initial changes. The trial period consisted of 15 repeated cycles, which included 20s exposure (2s on/2s off, alternating) and 60 second rest. The 2s on/2s off cycles were established to provide periods free of EMI within each measurement. The whole duration of a measurement was 24min.

After each measurement subjects communicated what number they had reached in counting backwards and guessed, whether he/she was exposed to radiation or not.

This study was approved by the Ethical Committee of the County of Zurich.

2.5. Subjects

2.6. Data analysis

The logarithm of the intensity data was taken and each light bundle was divided by the interoptode distance and a differential pathlength factor of 6.48 for 730nm and 5.82 for 830nm. The 2s on periods during exposure were detected and excluded from the analysis. Since during exposure periods, every other interval of 2s was missing, every other period of 2s was excluded also for the rest of the measurement. The data were averaged over periods of 2s. The mean and standard deviation of periods of 20s, i.e. five of these 2s intervals, were calculated.

Movement artifacts were detected and removed in the following way: If the standard deviation of a 20s period exceeded 2.5% of the mean, a movement artifact was assumed to be present. The respective period was rejected. A particular cycle thus consists of four periods of 20s, one before exposure (Pre), one during exposure (Exp), one directly after exposure (Post1) and one beginning 20s after exposure (Post2). If one period of 20s was rejected within a particular cycle the other three periods were rejected as well. The same is true for data at the other wavelength within a particular path.

From the remaining intensity data, concentration changes in O2Hb and HHb were calculated according to standard procedures [14

14. S. Wray, M. Cope, D. T. Delpy, J. S. Wyatt, and E. O. Reynolds, “Characterization of the near infrared absorption spectra of cytochrome aa3 and haemoglobin for the non-invasive monitoring of cerebral oxygenation,” Biochim. Biophys. Acta 933, 184–192 (1988). [CrossRef] [PubMed]

]. The Pre period was subtracted from the Exp, Post1, Post2 and following Pre periods to obtain changes due to exposure. A time triggered average was calculated for each subject and for each light path. For the assessment of short term effects (during exposure to up to 40s after exposure) we considered the Exp, Post1 and Post2 periods. For the assessment of long term effects the average difference between successive Pre periods was multiplied by the number of repetitions (15) to obtain the concentration change during the 20min of measurement. Since no localized changes within the sensor were discernible, all locations with the same interoptode distance were averaged for each subject.

The statistical analysis was carried out separately for the data of each interoptode distance. The differences between three exposures (12W/kg, 1.2W/kg, sham exposure) were analyzed by analysis of variance for each of the differences between Pre and the other periods. A difference was assumed to be statistically significant for p≤0.05. Furthermore, each combination of two types of exposure was compared by a nonparametric paired Wilcoxon test. Due to multiple testing (Bonferroni) a difference was assumed to be statistically significant for a reduced p≤0.016. The variances between the three types of exposure were compared using a Levene’s test.

To estimate the limit of detection, i.e. the size of the change we would have been able to detect, we first calculated the difference between the three exposure types for each parameter and distance and checked for normality of the distribution. Finally we calculated the 95% confidence interval (CI 95%) for these differences and averaged the values across the period of Exp, Post1 and Post2.

3. Results

3.1. Subjects

The data of two subjects were rejected due to too many movement artifacts. The remaining 16 subjects had a mean age of 31.2±6.3 (SD). 15 subjects were male and 14 right handed.

Between the different exposure types there was no significant difference for sleep quality, tiredness, caffeine or nicotine consumption, cell phone use, outside temperature, and inside temperature. Also the speed of the counting did not depend on the exposure type. Subjects were not able to guess, whether they were exposed to EMF or not, i.e. there was no significant correlation between the guess of the subjects and the true situation. This also can be seen as an indication for intact blinding.

3.2. Short term changes in O2Hb and HHb

The short term changes in O2Hb and HHb concentration during the three types of exposure are displayed in Fig. 3 to Fig. 6. There were a few significantly different means or variances between exposure types for the short term effects, which are identified in the captions and discussed below.

3.3. Long term changes in O2Hb and HHb

The long term changes in O2Hb and HHb concentration during the three types of exposure are displayed in Fig. 7. There were no significant differences between the three exposure types.

3.4. Detection limit

The CI 95% is an indicator of the detection limit of the current study. To estimate the size of effects that could have been detected, the CI 95% of the difference between the three exposure types was calculated and averaged. It was ±0.052µM (1.25cm distance), ±0.086µM (2.5cm) and ±0.138µM (3.75cm) for HHb and ±0.120µM (1.25cm distance), ±0.147µM (2.5cm) and ±0.198µM (3.75cm) for O2Hb.

Fig. 3. The short term changes in deoxyhemoglobin (HHb) concentration during three types of exposure: 12W/kg, 1.2W/kg and sham. Data were obtained from the left side of the head, which was towards the antenna. The symbols indicate the mean and the whiskers the 95% confidence interval (CI 95%). The results are displayed separately for the three different interoptode distances of 1.25cm (left), 2.5cm (middle) and 3.75cm (right). The depth of the interrogated tissue depends on the interoptode distance, i.e. the short distance of 1.25 mostly detects changes in the superficial layer of the head (skin and skull), while the longest distance also contains information about the brain. The data are displayed for the different periods of measurement: the 20s of exposure (Exp), the first 20s after exposure (Post1) and the second 20s after exposure (Post2). At 1.25cm the difference between HHb during exposure with 12W/kg and 1.2W/kg is statistically significant (p=0.011, Wilcoxon). Furthermore, the CI 95% between sham and 1.2W/kg was significantly different at 2.5cm distance for the Exp (p=0.001) and Post1 (p=0.015) periods. Otherwise there were no significant differences between the three types of exposure.
Fig. 4. The short term changes in oxyhemoglobin (O2Hb) concentration during three types of exposure: 12W/kg, 1.2W/kg and sham. Data were obtained from the left side of the head, which was towards the antenna. This figure is analogous to Fig. 3. At 2.5cm the difference between O2Hb during exposure with 12W/kg and sham is statistically significant (p=0.016, Wilcoxon). Otherwise there were no significant differences between the three types of exposure.
Fig. 5. The short term changes in deoxyhemoglobin (HHb) concentration during three types of exposure: 12W/kg, 1.2W/kg and sham. Data were obtained from the right side of the head, which was on the opposite side of the antenna. This figure is analogous to Fig. 3. The CI 95% between sham and 12W/kg was significantly different at 3.75cm distance for the Post1 (p=0.011) period. There were no other significant differences between the three types of exposure.
Fig. 6. The short term changes in oxyhemoglobin (O2Hb) concentration during three types of exposure: 12W/kg, 1.2W/kg and sham. Data were obtained from the right side of the head, which was on the opposite side of the antenna. This figure is analogous to Fig. 3. At 2.5cm the difference between O2Hb during exposure with 12W/kg and 1.2W/kg is statistically significant (p=0.009, Wilcoxon) and analysis of variance showed a significant difference between the three types of exposure during this period (p=0.009). Otherwise there were no significant differences between the three types of exposure.
Fig. 7. The long term changes over 20min in oxyhemoglobin (O2Hb) and deoxyhemoglobin (HHb) concentration during three types of exposure: 12W/kg, 1.2W/kg and sham. The results are displayed separately for the three different interoptode distances of 1.25cm (left), 2.5cm (middle) and 3.75cm (right). The data from the left side was close to the antenna while the right side was further away form the antenna. The whiskers correspond to the CI 95%. There were no significant differences between the three exposure types.

4. Discussion

4.1. General consideration about NIRS data

The current study applies one specific signal at 900MHz using a defined temporal pattern to study a potential alteration in blood circulation. The protocol of this study was similar to the ones used for studying functional activity of the brain. In principle several types of NIRS methods could have been used for this study. CBF can be assessed using a tracer, diffusing-wave spectroscopy (DWS) and standard NIRS. As tracers O2 [17

17. A. D. Edwards, C. Richardson, P. van der Zee, C. Elwell, J. S. Wyatt, M. Cope, D. T. Delpy, and E. O. Reynolds, “Measurement of hemoglobin flow and blood flow by near-infrared spectroscopy,” J. Appl. Physiol. 75, 1884–1889 (1993). [PubMed]

, 18

18. M. Wolf, N. Brun, G. Greisen, M. Keel, K. von Siebenthal, and H. Bucher, “Optimising the methodology of calculating the cerebral blood flow of newborn infants from near infra-red spectrophotometry data,” Med. Biol. Eng. Comput. 34, 221–226 (1996). [CrossRef] [PubMed]

] and indocyanine green [19

19. E. Keller, A. Nadler, H. Alkadhi, S. S. Kollias, Y. Yonekawa, and P. Niederer, “Noninvasive measurement of regional cerebral blood flow and regional cerebral blood volume by near-infrared spectroscopy and indocyanine green dye dilution,” Neuroimage 20, 828–839 (2003). [CrossRef] [PubMed]

, 20

20. A. Liebert, H. Wabnitz, J. Steinbrink, M. Moller, R. Macdonald, H. Rinneberg, A. Villringer, and H. Obrig, “Bed-side assessment of cerebral perfusion in stroke patients based on optical monitoring of a dye bolus by time-resolved diffuse reflectance,” Neuroimage 24, 426–435 (2005). [CrossRef] [PubMed]

] have been used. O2 is only feasible as a tracer in mechanically ventilated subjects, while an injection of indocyanine green could also be applied to study a healthy population. As all methods using a tracer, the time resolution is low (~min), because every measurement requires an injection of the tracer, which is the reason why we did not employ this method. DWS is a method based on the analysis of variation in the speckle interference patterns. The amount of variation depends on the blood flow in tissue. Although DWS [21

21. T. Durduran, G. Yu, M. G. Burnett, J. A. Detre, J. H. Greenberg, J. Wang, C. Zhou, and A. G. Yodh, “Diffuse optical measurement of blood flow, blood oxygenation, and metabolism in a human brain during sensorimotor cortex activation,” Opt. Lett. 29, 1766–1768 (2004). [CrossRef] [PubMed]

, 22

22. J. Li, G. Dietsche, D. Iftime, S. E. Skipetrov, G. Maret, T. Elbert, B. Rockstroh, and T. Gisler, “Noninvasive detection of functional brain activity with near-infrared diffusing-wave spectroscopy,” J. Biomed. Opt. 10, 44002 (2005). [CrossRef] [PubMed]

] is a sensitive method with a high time resolution, which could in principle also image CBF, the interpretation of the dynamic data depends on the optical parameters of tissue. In addition to obtain information on the CBV and oxygenation a separate NIRS system needs to be added. In the future DWS may become the tool of choice, however, at the moment we preferred to apply the more established standard NIRS technique, which is also highly sensitive, has a high time resolution and images changes in O2Hb and HHb concentration. From these changes in CBF and CBV can be derived with standard NIRS as outlined above and the algorithms are quite well established. The standard NIRS system including the sensor geometry used in this study was specifically built for functional studies.

NIR light has a limited penetration depth, which depends on the interoptode distance, for 1.25cm the mean penetration depth is approximately 5mm and for 3.75cm it is 9mm. However, photons travel much deeper, information from the brain activity at a depth of 2.5cm was previously detected [11

11. V. Toronov, A. Webb, J. H. Choi, M. Wolf, A. Michalos, E. Gratton, and D. Hueber, “Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging,” Med. Phys. 28, 521–527 (2001). [CrossRef] [PubMed]

]. Currently work is in progress to resolve different layers of tissue. Noise increases with interoptode distance. The optimal distance is a tradeoff between sensitivity to deep layers of tissue and signal to noise ratio. Therefore, typically for studies of functional activity a distance of 2.5 to 3.0cm is used.

For the interpretation of the data, it is helpful to keep in mind the regular changes of blood circulation of the brain as assessed by functional NIRS or other regular physiological processes. The peak magnitude of the changes in O2Hb and HHb functional activity in the motor cortex assessed with our instrument corresponds to approximately 0.85µM or 0.25µM, respectively, which is in agreement with other published data [10

10. M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi, and E. Gratton, “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study,” Neuroimage 16, 704–712 (2002). [CrossRef] [PubMed]

, 23

23. A. Maki, Y. Yamashita, E. Watanabe, and H. Koizumi, “Visualizing human motor activity by using noninvasive optical topography,” Front. Med. Biol. Eng. 7, 285–297 (1996). [PubMed]

25

25. W. N. Colier, V. Quaresima, B. Oeseburg, and M. Ferrari, “Human motor-cortex oxygenation changes induced by cyclic coupled movements of hand and foot,” Exp. Brain. Res. 129, 457–461 (1999). [CrossRef] [PubMed]

]. In the visual cortex usually higher magnitudes of 2.5µM for O2Hb and 0.6µM for HHb were found [10

10. M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi, and E. Gratton, “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study,” Neuroimage 16, 704–712 (2002). [CrossRef] [PubMed]

, 26

26. H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhaupl, and A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults,” Neuroimage 12, 623–639 (2000). [CrossRef] [PubMed]

, 27

27. P. Wobst, R. Wenzel, M. Kohl, H. Obrig, and A. Villringer, “Linear aspects of changes in deoxygenated hemoglobin concentration and cytochrome oxidase oxidation during brain activation,” Neuroimage 13, 520–530 (2001). [CrossRef] [PubMed]

].

Another regular physiological process is the change in O2Hb and HHb concentration due to slow vasomotion. This occurs at a rate 1/10s. The magnitude of these changes is in the order of 1µM for O2Hb and 0.2µM for HHb [26

26. H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhaupl, and A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults,” Neuroimage 12, 623–639 (2000). [CrossRef] [PubMed]

].

The regular cerebral tHb concentration corresponds to approximately 73.8±15.0µM [28

28. M. Wolf, P. Evans, H. U. Bucher, V. Dietz, M. Keel, R. Strebel, and K. von Siebenthal, “Measurement of absolute cerebral haemoglobin concentration in adults and neonates,” Adv. Exp. Med. Biol. 428, 219–227 (1997). [CrossRef] [PubMed]

].

4.2. Long term changes in O2Hb and HHb

Whatever the origin of the effect leading to this drift is, there were no significant differences in the drift between the different types of exposure. Thus, there were no long term effects of exposure visible.

4.3. Short term changes in O2Hb and HHb

The long term drift is overlayed to the short term effects displayed in Fig. 3 to Fig. 6. Keep in mind that this drift is usually eliminated by a detrending operation in publications describing functional brain activity. We did not detrend the data to avoid any spurious effects.

We found the following three significant short term concentration changes: On the left exposed side of the head at 1.25cm (Fig. 3), the difference between HHb concentration during exposure with 12W/kg and 1.2W/kg was statistically significant (p=0.011, Wilcoxon, reduced significance level of 0.016 due to Bonferroni). On the left exposed side of the head at 2.5cm (Fig. 4) the difference between O2Hb during exposure with 12W/kg and sham is just statistically significant (p=0.016, Wilcoxon). On the right side of the head, which was on the opposite side of the antenna (Fig. 6), at 2.5cm the difference between O2Hb during exposure with 12W/kg and 1.2W/kg is statistically significant (p=0.009, Wilcoxon) and analysis of variance showed a significant difference between the three types of exposure during this period (p=0.009).

For the CI 95% there were again three significant differences between exposure types. On the left exposed side of the head for HHb at 2.5cm (Fig. 3), the CI 95% was significantly different between 12W/kg and 1.2W/kg for the EXP (p=0.001) and Post1 (p=0.015) periods (Fig. 3). On the right side of the head, at 3.75cm for O2Hb the CI 95% was significantly different (p=0.011) between 12W/kg and 1.2W/kg for the Post1 period (Fig. 5).

Otherwise there were no significant differences between the three types of exposure.

How can these statistically significant differences be interpreted? The p-value gives the likelihood of a statistical type I error, i.e. the probability to detect a difference by chance, when in reality there is none. Multiple testing increases the risk of obtaining significant differences by pure chance, e.g. for 20 tests we will detect one statistically significant (p=0.05) difference just by chance. For the analysis of variance, we carried out 36 tests (3 exposure types ×3 distances ×2 sides ×2 substances). Thus we expect approximately two significant differences just by chance. The same likelihood applies for CI 95% and the paired Wilcoxon test. Since none of the detected significance levels is high, we conclude that there is a high probability that the detected significances are due to chance.

Keeping this in mind let us consider, what the physiological meaning of the significant differences would be, if they were real. The general pattern of the differences is a decrease in O2Hb and a smaller increase in HHb with exposure to EMF. This indicates a decrease in blood flow and blood volume. Thus, a thermal short term effect of EMF can be excluded, because it would lead to an increase in blood flow and volume. Since most of the significant differences were found at a distance of 2.5cm, they are unlikely to originate from superficial layers of tissue (see also below).

The significant increase in HHb on the exposed hemisphere at 1.25cm distance is very small (0.026µM), i.e. five times smaller than the response to functional activity. It is noteworthy that it is the difference between 1.2Wkg exposure and 12W/kg exposure and that the difference to sham is not significant.

The significant decrease in O2Hb on the exposed hemisphere at 2.5cm distance is also very small (0.102µM), i.e. eight times smaller than the response to functional activity. This difference occurs between sham and 12W/kg exposure.

The significant decrease in O2Hb on the opposite hemisphere of the exposure at 2.5cm distance is also small (0.156µM), i.e. six times smaller than the response to functional activity. Again it is noteworthy that it is the difference between 1.2Wkg exposure and 12W/kg exposure and that the difference to sham is not significant. Unless we postulate that a low dose leads to the opposite effect of a higher dose of EMF, this pattern seems to be unlikely. Furthermore, the dose of EMF on the opposite hemisphere was approximately eight times lower than on the exposed side. It is difficult to explain, why an effect on the opposite side of the exposure should be larger than on the exposed side.

Thus in conclusion, in the unlikely case that the effects found were true, they would be between 5.4 to 9.6 times smaller than the effects due to functional activation and they would indicate a decrease in blood flow and volume, which corresponds to the effects of a deactivation of the brain [29

29. R. Wenzel, P. Wobst, H. H. Heekeren, K. K. Kwong, S. A. Brandt, M. Kohl, H. Obrig, U. Dirnagl, and A. Villringer, “Saccadic suppression induces focal hypooxygenation in the occipital cortex,” J. Cereb. Blood Flow Metab. 20(7), 1103–1110 (2000). [CrossRef]

].

4.4. Detection limit

The difference between the three exposure types that could have been detected compared to changes associated with regular functional activity, is approximately five times (1.25cm distance), three times (2.5cm) and twice (3.75cm) lower for HHb and seven times (1.25cm distance), six times (2.5cm) and four times (3.75cm) lower for O2Hb. Thus, we can exclude that the change due to exposure with EMF exceeds these limits. This also means that, if there was a change in blood circulation due to EMF, it would certainly be smaller than the regular physiological changes.

The detection limit increases with the distance, because the noise level increases with distance. The significant changes in O2Hb observed at a distance of 2.5cm originate from deeper layers of tissue, because at 1.25cm no significant change was observed even though at such a distance a superficial change would have been detected with a higher statistical power.

In principle the detection limit depends on the noise level of the different components of the measurement: the instrumental noise including shot noise, the physiological noise and the noise due to movement artifacts. According to the previously published [13

13. D. Haensse, P. Szabo, D. Brown, J. C. Fauchere, P. Niederer, H. U. Bucher, and M. Wolf, “New multichannel near infrared spectrophotometry system for functional studies of the brain in adults and neonates,” Opt. Express 13, 4525–4538 (2005). [CrossRef] [PubMed]

] values the instrumental noise for our set-up is below 0.001µM. Thus the main sources of noise in our measurements were the physiological noise and the noise due to movement artifacts.

In conclusion the low noise level of our measurements allows detecting changes in blood circulation, which are one seventh to one half of the regular physiological changes in blood circulation.

4.5. Limitations and strengths of NIRS

NIRS is mostly sensitive to capillaries [30

30. C. D. Kurth, H. Liu, W. S. Thayer, and B. Chance, “A dynamic phantom brain model for near-infrared spectroscopy,” Phys. Med. Biol. 40, 2079–2092 (1995). [CrossRef] [PubMed]

], which is an advantage compared to other methods such as PET or MRI, because it is more sensitive to local effects.

The current instrument is sensitive to EMI, because there are electronic devices in the sensor. The interferences could be eliminated by using fiber optics. This would enable to analyze potential effects at a higher time resolution (e.g. 100Hz). In addition this would enable to remove some of the physiological noise caused by the heart beat and thus increase the signal to noise ratio.

Short term effects have to the best of our knowledge not been studied so far.

In the latest PET study [6

6. S. Aalto, C. Haarala, A. Bruck, H. Sipila, H. Hamalainen, and J. O. Rinne, “Mobile phone affects cerebral blood flow in humans,” J. Cereb. Blood Flow Metab. (2006).

], a decrease in CBF was found beneath the antenna, which may seem to confirm our results. However, the set-up used in this study was considerably different from our set-up concerning the exposure system, the task and the timing.

5. Conclusions

Borderline significant immediate responses of O2Hb and HHb to EMF were found, i.e. within 20s during exposure. These responses correspond to a decrease of CBF and CBV. They were much smaller than regular physiological changes in O2Hb and HHb elicited e.g. by functional activation of the brain. As discussed above there is a high probability that these responses are due to chance. Therefore, these effects require further studies.

There was no detectable response of O2Hb or/and HHb to EMF within 40s after exposure. There was no detectable dose-response relation. No lateral differences have been found. The detection limit was a fraction of the regular physiological changes elicited by functional activation. There was no detectable slow response of O2Hb or/and HHb to EMF, which occurs within 20min.

Acknowledgements

We gratefully acknowledge the funding for this research project by the Research Foundation Mobile Communication at the Swiss Federal Institute of Technology in Zurich (ETHZ) and by the Federal Office of Public Health, which also supported the pilot study as well as the extension of the main study. We thank Dr. Derek Brown, Dr. Jürgen Schuderer, Sven Ebert and Dr. Walter Oesch for their technical support and assistance. We also would like to thank PD Dr. Peter Achermann, Dr. Mirjana Moser and Prof. Niels Kuster for valuable advice and all the subjects for their participation.

References and links

1.

A. A. Borbely, R. Huber, T. Graf, B. Fuchs, E. Gallmann, and P. Achermann, “Pulsed high-frequency electromagnetic field affects human sleep and sleep electroencephalogram,” Neurosci. Lett. 275, 207–210 (1999). [CrossRef] [PubMed]

2.

R. Huber, T. Graf, K. A. Cote, L. Wittmann, E. Gallmann, D. Matter, J. Schuderer, N. Kuster, A. A. Borbely, and P. Achermann, “Exposure to pulsed high-frequency electromagnetic field during waking affects human sleep EEG,” Neuroreport 11, 3321–3325 (2000). [CrossRef] [PubMed]

3.

R. Huber, V. Treyer, A. A. Borbely, J. Schuderer, J. M. Gottselig, H. P. Landolt, E. Werth, T. Berthold, N. Kuster, A. Buck, and P. Achermann, “Electromagnetic fields, such as those from mobile phones, alter regional cerebral blood flow and sleep and waking EEG,” J. Sleep. Res. 11, 289–295 (2002). [CrossRef] [PubMed]

4.

R. Huber, J. Schuderer, T. Graf, K. Jutz, A. A. Borbely, N. Kuster, and P. Achermann, “Radio frequency electromagnetic field exposure in humans: Estimation of SAR distribution in the brain, effects on sleep and heart rate,” Bioelectromagnetics 24, 262–276 (2003). [CrossRef] [PubMed]

5.

R. Huber, V. Treyer, J. Schuderer, T. Berthold, A. Buck, N. Kuster, H. P. Landolt, and P. Achermann, “Exposure to pulse-modulated radio frequency electromagnetic fields affects regional cerebral blood flow,” Eur. J. Neurosci. 21, 1000–1006 (2005). [CrossRef] [PubMed]

6.

S. Aalto, C. Haarala, A. Bruck, H. Sipila, H. Hamalainen, and J. O. Rinne, “Mobile phone affects cerebral blood flow in humans,” J. Cereb. Blood Flow Metab. (2006).

7.

H. Obrig and A. Villringer, “Beyond the visible--imaging the human brain with light,” J. Cereb. Blood Flow Metab. 23, 1–18 (2003). [CrossRef]

8.

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20, 435–442 (1997). [CrossRef] [PubMed]

9.

Y. Hoshi, “Functional near-infrared optical imaging: utility and limitations in human brain mapping,” Psychophysiology 40, 511–520 (2003). [CrossRef] [PubMed]

10.

M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi, and E. Gratton, “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study,” Neuroimage 16, 704–712 (2002). [CrossRef] [PubMed]

11.

V. Toronov, A. Webb, J. H. Choi, M. Wolf, A. Michalos, E. Gratton, and D. Hueber, “Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging,” Med. Phys. 28, 521–527 (2001). [CrossRef] [PubMed]

12.

K. Villringer, S. Minoshima, C. Hock, H. Obrig, S. Ziegler, U. Dirnagl, M. Schwaiger, and A. Villringer, “Assessment of local brain activation. A simultaneous PET and near-infrared spectroscopy study,” Adv. Exp. Med. Biol. 413, 149–153 (1997). [PubMed]

13.

D. Haensse, P. Szabo, D. Brown, J. C. Fauchere, P. Niederer, H. U. Bucher, and M. Wolf, “New multichannel near infrared spectrophotometry system for functional studies of the brain in adults and neonates,” Opt. Express 13, 4525–4538 (2005). [CrossRef] [PubMed]

14.

S. Wray, M. Cope, D. T. Delpy, J. S. Wyatt, and E. O. Reynolds, “Characterization of the near infrared absorption spectra of cytochrome aa3 and haemoglobin for the non-invasive monitoring of cerebral oxygenation,” Biochim. Biophys. Acta 933, 184–192 (1988). [CrossRef] [PubMed]

15.

S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227, 54–68 (1995). [CrossRef] [PubMed]

16.

A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39, 889–894 (1996). [CrossRef] [PubMed]

17.

A. D. Edwards, C. Richardson, P. van der Zee, C. Elwell, J. S. Wyatt, M. Cope, D. T. Delpy, and E. O. Reynolds, “Measurement of hemoglobin flow and blood flow by near-infrared spectroscopy,” J. Appl. Physiol. 75, 1884–1889 (1993). [PubMed]

18.

M. Wolf, N. Brun, G. Greisen, M. Keel, K. von Siebenthal, and H. Bucher, “Optimising the methodology of calculating the cerebral blood flow of newborn infants from near infra-red spectrophotometry data,” Med. Biol. Eng. Comput. 34, 221–226 (1996). [CrossRef] [PubMed]

19.

E. Keller, A. Nadler, H. Alkadhi, S. S. Kollias, Y. Yonekawa, and P. Niederer, “Noninvasive measurement of regional cerebral blood flow and regional cerebral blood volume by near-infrared spectroscopy and indocyanine green dye dilution,” Neuroimage 20, 828–839 (2003). [CrossRef] [PubMed]

20.

A. Liebert, H. Wabnitz, J. Steinbrink, M. Moller, R. Macdonald, H. Rinneberg, A. Villringer, and H. Obrig, “Bed-side assessment of cerebral perfusion in stroke patients based on optical monitoring of a dye bolus by time-resolved diffuse reflectance,” Neuroimage 24, 426–435 (2005). [CrossRef] [PubMed]

21.

T. Durduran, G. Yu, M. G. Burnett, J. A. Detre, J. H. Greenberg, J. Wang, C. Zhou, and A. G. Yodh, “Diffuse optical measurement of blood flow, blood oxygenation, and metabolism in a human brain during sensorimotor cortex activation,” Opt. Lett. 29, 1766–1768 (2004). [CrossRef] [PubMed]

22.

J. Li, G. Dietsche, D. Iftime, S. E. Skipetrov, G. Maret, T. Elbert, B. Rockstroh, and T. Gisler, “Noninvasive detection of functional brain activity with near-infrared diffusing-wave spectroscopy,” J. Biomed. Opt. 10, 44002 (2005). [CrossRef] [PubMed]

23.

A. Maki, Y. Yamashita, E. Watanabe, and H. Koizumi, “Visualizing human motor activity by using noninvasive optical topography,” Front. Med. Biol. Eng. 7, 285–297 (1996). [PubMed]

24.

T. S. Leung, C. E. Elwell, J. R. Henty, and D. T. Delpy, “Simultaneous measurement of cerebral tissue oxygenation over the adult frontal and motor cortex during rest and functional activation,” Adv. Exp. Med. Biol. 510, 385–389 (2003). [CrossRef] [PubMed]

25.

W. N. Colier, V. Quaresima, B. Oeseburg, and M. Ferrari, “Human motor-cortex oxygenation changes induced by cyclic coupled movements of hand and foot,” Exp. Brain. Res. 129, 457–461 (1999). [CrossRef] [PubMed]

26.

H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhaupl, and A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults,” Neuroimage 12, 623–639 (2000). [CrossRef] [PubMed]

27.

P. Wobst, R. Wenzel, M. Kohl, H. Obrig, and A. Villringer, “Linear aspects of changes in deoxygenated hemoglobin concentration and cytochrome oxidase oxidation during brain activation,” Neuroimage 13, 520–530 (2001). [CrossRef] [PubMed]

28.

M. Wolf, P. Evans, H. U. Bucher, V. Dietz, M. Keel, R. Strebel, and K. von Siebenthal, “Measurement of absolute cerebral haemoglobin concentration in adults and neonates,” Adv. Exp. Med. Biol. 428, 219–227 (1997). [CrossRef] [PubMed]

29.

R. Wenzel, P. Wobst, H. H. Heekeren, K. K. Kwong, S. A. Brandt, M. Kohl, H. Obrig, U. Dirnagl, and A. Villringer, “Saccadic suppression induces focal hypooxygenation in the occipital cortex,” J. Cereb. Blood Flow Metab. 20(7), 1103–1110 (2000). [CrossRef]

30.

C. D. Kurth, H. Liu, W. S. Thayer, and B. Chance, “A dynamic phantom brain model for near-infrared spectroscopy,” Phys. Med. Biol. 40, 2079–2092 (1995). [CrossRef] [PubMed]

OCIS Codes
(170.1470) Medical optics and biotechnology : Blood or tissue constituent monitoring

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: April 28, 2006
Revised Manuscript: June 16, 2006
Manuscript Accepted: June 16, 2006
Published: June 26, 2006

Virtual Issues
Vol. 1, Iss. 7 Virtual Journal for Biomedical Optics

Citation
Martin Wolf, Daniel Haensse, Geert Morren, and Juerg Froehlich, "Do GSM 900MHz signals affect cerebral blood circulation? A near-infrared spectrophotometry study," Opt. Express 14, 6128-6141 (2006)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-14-13-6128


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References

  1. A. A. Borbely, R. Huber, T. Graf, B. Fuchs, E. Gallmann, and P. Achermann, "Pulsed high-frequency electromagnetic field affects human sleep and sleep electroencephalogram," Neurosci. Lett. 275, 207-210 (1999). [CrossRef] [PubMed]
  2. R. Huber, T. Graf, K. A. Cote, L. Wittmann, E. Gallmann, D. Matter, J. Schuderer, N. Kuster, A. A. Borbely, and P. Achermann, "Exposure to pulsed high-frequency electromagnetic field during waking affects human sleep EEG," Neuroreport 11, 3321-3325 (2000). [CrossRef] [PubMed]
  3. R. Huber, V. Treyer, A. A. Borbely, J. Schuderer, J. M. Gottselig, H. P. Landolt, E. Werth, T. Berthold, N. Kuster, A. Buck, and P. Achermann, "Electromagnetic fields, such as those from mobile phones, alter regional cerebral blood flow and sleep and waking EEG," J. Sleep. Res. 11, 289-295 (2002). [CrossRef] [PubMed]
  4. R. Huber, J. Schuderer, T. Graf, K. Jutz, A. A. Borbely, N. Kuster, and P. Achermann, "Radio frequency electromagnetic field exposure in humans: Estimation of SAR distribution in the brain, effects on sleep and heart rate," Bioelectromagnetics 24, 262-276 (2003). [CrossRef] [PubMed]
  5. R. Huber, V. Treyer, J. Schuderer, T. Berthold, A. Buck, N. Kuster, H. P. Landolt, and P. Achermann, "Exposure to pulse-modulated radio frequency electromagnetic fields affects regional cerebral blood flow," Eur. J. Neurosci. 21, 1000-1006 (2005). [CrossRef] [PubMed]
  6. S. Aalto, C. Haarala, A. Bruck, H. Sipila, H. Hamalainen, and J. O. Rinne, "Mobile phone affects cerebral blood flow in humans," J. Cereb. Blood Flow Metab. (2006).
  7. H. Obrig and A. Villringer, "Beyond the visible--imaging the human brain with light," J. Cereb. Blood Flow Metab. 23, 1-18 (2003). [CrossRef]
  8. A. Villringer and B. Chance, "Non-invasive optical spectroscopy and imaging of human brain function," Trends Neurosci. 20, 435-442 (1997). [CrossRef] [PubMed]
  9. Y. Hoshi, "Functional near-infrared optical imaging: utility and limitations in human brain mapping," Psychophysiology 40, 511-520 (2003). [CrossRef] [PubMed]
  10. M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi, and E. Gratton, "Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study," Neuroimage 16, 704-712 (2002). [CrossRef] [PubMed]
  11. V. Toronov, A. Webb, J. H. Choi, M. Wolf, A. Michalos, E. Gratton, and D. Hueber, "Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging," Med. Phys. 28, 521-527 (2001). [CrossRef] [PubMed]
  12. K. Villringer, S. Minoshima, C. Hock, H. Obrig, S. Ziegler, U. Dirnagl, M. Schwaiger, and A. Villringer, "Assessment of local brain activation. A simultaneous PET and near-infrared spectroscopy study," Adv. Exp. Med. Biol. 413, 149-153 (1997). [PubMed]
  13. D. Haensse, P. Szabo, D. Brown, J. C. Fauchere, P. Niederer, H. U. Bucher, and M. Wolf, "New multichannel near infrared spectrophotometry system for functional studies of the brain in adults and neonates," Opt. Express 13, 4525-4538 (2005). [CrossRef] [PubMed]
  14. S. Wray, M. Cope, D. T. Delpy, J. S. Wyatt, and E. O. Reynolds, "Characterization of the near infrared absorption spectra of cytochrome aa3 and haemoglobin for the non-invasive monitoring of cerebral oxygenation," Biochim. Biophys. Acta 933, 184-192 (1988). [CrossRef] [PubMed]
  15. S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, "Performance comparison of several published tissue near-infrared spectroscopy algorithms," Anal. Biochem. 227, 54-68 (1995). [CrossRef] [PubMed]
  16. A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, "Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy," Pediatr. Res. 39, 889-894 (1996). [CrossRef] [PubMed]
  17. A. D. Edwards, C. Richardson, P. van der Zee, C. Elwell, J. S. Wyatt, M. Cope, D. T. Delpy, and E. O. Reynolds, "Measurement of hemoglobin flow and blood flow by near-infrared spectroscopy," J. Appl. Physiol. 75, 1884-1889 (1993). [PubMed]
  18. M. Wolf, N. Brun, G. Greisen, M. Keel, K. von Siebenthal, and H. Bucher, "Optimising the methodology of calculating the cerebral blood flow of newborn infants from near infra-red spectrophotometry data," Med. Biol. Eng. Comput. 34, 221-226 (1996). [CrossRef] [PubMed]
  19. E. Keller, A. Nadler, H. Alkadhi, S. S. Kollias, Y. Yonekawa, and P. Niederer, "Noninvasive measurement of regional cerebral blood flow and regional cerebral blood volume by near-infrared spectroscopy and indocyanine green dye dilution," Neuroimage 20, 828-839 (2003). [CrossRef] [PubMed]
  20. A. Liebert, H. Wabnitz, J. Steinbrink, M. Moller, R. Macdonald, H. Rinneberg, A. Villringer, and H. Obrig, "Bed-side assessment of cerebral perfusion in stroke patients based on optical monitoring of a dye bolus by time-resolved diffuse reflectance," Neuroimage 24, 426-435 (2005). [CrossRef] [PubMed]
  21. T. Durduran, G. Yu, M. G. Burnett, J. A. Detre, J. H. Greenberg, J. Wang, C. Zhou, and A. G. Yodh, "Diffuse optical measurement of blood flow, blood oxygenation, and metabolism in a human brain during sensorimotor cortex activation," Opt. Lett. 29, 1766-1768 (2004). [CrossRef] [PubMed]
  22. J. Li, G. Dietsche, D. Iftime, S. E. Skipetrov, G. Maret, T. Elbert, B. Rockstroh, and T. Gisler, "Noninvasive detection of functional brain activity with near-infrared diffusing-wave spectroscopy," J. Biomed. Opt. 10, 44002 (2005). [CrossRef] [PubMed]
  23. A. Maki, Y. Yamashita, E. Watanabe, and H. Koizumi, "Visualizing human motor activity by using non-invasive optical topography," Front. Med. Biol. Eng. 7, 285-297 (1996). [PubMed]
  24. T. S. Leung, C. E. Elwell, J. R. Henty, and D. T. Delpy, "Simultaneous measurement of cerebral tissue oxygenation over the adult frontal and motor cortex during rest and functional activation," Adv. Exp. Med. Biol. 510, 385-389 (2003). [CrossRef] [PubMed]
  25. W. N. Colier, V. Quaresima, B. Oeseburg, and M. Ferrari, "Human motor-cortex oxygenation changes induced by cyclic coupled movements of hand and foot," Exp. Brain. Res. 129, 457-461 (1999). [CrossRef] [PubMed]
  26. H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhaupl, and A. Villringer, "Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults," Neuroimage 12, 623-639 (2000). [CrossRef] [PubMed]
  27. P. Wobst, R. Wenzel, M. Kohl, H. Obrig, and A. Villringer, "Linear aspects of changes in deoxygenated hemoglobin concentration and cytochrome oxidase oxidation during brain activation," Neuroimage 13, 520-530 (2001). [CrossRef] [PubMed]
  28. M. Wolf, P. Evans, H. U. Bucher, V. Dietz, M. Keel, R. Strebel, and K. von Siebenthal, "Measurement of absolute cerebral haemoglobin concentration in adults and neonates," Adv. Exp. Med. Biol. 428, 219-227 (1997). [CrossRef] [PubMed]
  29. R. Wenzel, P. Wobst, H. H. Heekeren, K. K. Kwong, S. A. Brandt, M. Kohl, H. Obrig, U. Dirnagl, and A. Villringer, "Saccadic suppression induces focal hypooxygenation in the occipital cortex," J. Cereb. Blood Flow Metab. 20, 1103-1110 (2000). [CrossRef]
  30. C. D. Kurth, H. Liu, W. S. Thayer, and B. Chance, "A dynamic phantom brain model for near-infrared spectroscopy," Phys. Med. Biol. 40, 2079-2092 (1995). [CrossRef] [PubMed]

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