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Chapter 8 High-frequency oscillatory activities during selective attention in humans
Functional Neuroscience: Evoked Potentials and Related Techniques (Supplements to Clinical Neurophysiology, Vol. 59) Editors: C. Barber, S. Tsuji, S. Tobimatsu, T. Uozumi, N. Akamatsu, A. Eisen © 2006 Elsevier B.V. All rights reserved
57
Chapter 8
High-frequency oscillatory activities during selective attention in humans Isamu Ozakia,*, Yukoh Yaegashib, Masayuki Babac and Isao Hashimotod a
Faculty of Health Sciences, Aomori University of Health and Welfare, Aomori (Japan) b Department of Social Science, Akita Keijoh College, Oodate (Japan) c Department of Neurological Sciences, Hirosaki University School of Medicine, Hirosaki (Japan) d Human Information Systems Laboratory, Kanazawa Institute of Technology, Tokyo (Japan)
1. Introduction
2. Subjects and methods
The frequency of spontaneous electroencephalogram (EEG) is known to change with the level of vigilance: the low-frequency alpha wave is dominant at rest and the high-frequency gamma oscillation (mainly 30–40 Hz) appears at a high vigilance level. However, little attention has been given to changes in very high-frequency (>100 Hz) activities. Recent studies on somatosensory evoked magnetic fields or potentials disclosed that the initial cortical response contains a high-frequency (HF) component at around 600 Hz (Curio et al., 1994; Hashimoto et al., 1996; Ozaki et al., 1998), and the power of HF component changes during a sleep–waking cycle (Yamada et al., 1988; Hashimoto et al., 1996); however, there have been few reports on HF component changes during attentive tasks. We therefore tested whether EEG activities at extreme HF ranges alter in selective attention to stimuli using somatosensory evoked potentials (SEPs).
Seven normal adults (3 men, 4 women) participated in the experiments. The mean age was 29 years (range 20–45 years). The subject sat relaxed in a comfortable reclining chair in a quiet, air-conditioned, and electrically shielded room. During the recording session, the subject was encouraged to minimize muscle and eye blink interference and was kept awake. All the subjects gave their informed consent. Brief electric shock (0.2 ms duration) was applied to the right median nerve at the wrist at a rate of 5 stimuli/s. Intensity was adjusted so as to induce a small muscular twitch in the thenar muscles. SEPs were recorded from the frontal scalp at Fz (International 10–20 system), the ipsilateral central scalp at C4, and six electrodes covering the left centro-parietal scalp at FC3 (3 cm anterior to C3), C3, CP3 (3 cm posterior to C3), and 2 cm medial to FC3, C3, or CP3 (see Fig. 1). The right ear served as a reference. SEPs of 100 ms (5 ms before stimuli) were digitized at a 20 kHz sampling rate. In attentive sessions, we omitted the stimuli in a random sequence, and the subject was instructed to count omitting stimuli in their head. In neglect sessions, the subject listened to their favorite music to try to ignore the electric stimuli. For each run, about 1000 trials
*Correspondence to: Dr. Isamu Ozaki, Faculty of Health Sciences, Aomori University of Health and Welfare, 58-1 Mase, Hamadate, Aomori 030-8505, Japan. Tel/Fax: +81-17-765-2070; E-mail: [email protected]
58
Fig. 1. The position of the 8 electrodes over the scalp. Channel 7 (Ch 7): Fz; Ch 1: 3 cm anterior and 2 cm medial to C3; Ch 2: 3 cm anterior to C3; Ch 3: 2 cm medial to C3; Ch 4: C3; Ch 5: 3 cm posterior and 2 cm medial to C3; Ch 6: 3 cm posterior to C3; Ch 8: C4.
were averaged with a Dantec Keypoint electromyograph. Data were stored on floppy disks and converted to IBM format for later off-line analysis. We recorded 5–7 runs for attentive or neglect sessions. Then, grand averaging of attentive or neglect sessions was performed in each subject. The original filter setting was 0.5–2000 Hz. In off-line analysis, using a Hamming window, digital bandpass filtering of 300–1000 Hz was performed to separate high frequency oscillations (HFOs) from the underlying initial cortical response such as the N20 potential. Using a maximum entropy method (Ulrych, 1972), a power spectrum with a 10 ms period was calculated as the logarithm of power squared in base 10 for each frequency at every 1 ms interval. A time–frequency analysis of HF components was then obtained. For statistical analysis of HF components, we performed 3-way (channel × subject × condition) ANOVA. 3. Results Figure 2 shows SEP traces recorded from the left parietal scalp in a representative subject. There were no significant changes in the original wide-band traces between attentive and neglect conditions. However, for the post-filtered traces, HF activities at 300–400 Hz were dominant under attentive conditions. They appeared after 30 ms and lasted up to 60 ms under
Fig. 2. Effect of attention on SEPs to right median nerve stimulation in a 23-year-old woman. Top panel: the original SEP traces recorded from the left parietal scalp are compared between attentive and neglect conditions. The thick trace shows the attentive condition, and the thin trace, the neglect condition. Middle and bottom panels: the postfiltered traces of 8 channels for the attentive condition (middle) and neglect condition (bottom) are superimposed. Note that although HFOs overlying N20 seem unchanged, high-frequency activity at 300–400 Hz appears after 30 ms and lasts up to 60 ms in the attentive condition (small arrows).
attentive conditions, although HFOs overlying N20 seemed unchanged. Although the HF activities appeared at around 60 ms under neglect conditions, they did not last long compared with those under attentive conditions. In Fig. 3, the results of time–frequency analysis for the left parietal scalp recording in the same subject as in Fig. 1 are illustrated. HF activity at approximately 350 Hz was discerned under attentive conditions. HF activity also appeared at around 30 ms and lasted up to 55 ms in other recording channels under attentive conditions. For HFOs overlying N20–P20 and central P22 potentials, there were no obvious changes between attentive and neglect conditions in the subjects examined.
59 and a mean appearance time of 40 ms with 95% confidence interval between 35 and 45 ms. Figure 4 shows the time–frequency analysis of the left fronto-central scalp recording of the grand averaged data for 7 subjects. The dominant HF activity at around 350 Hz was discerned under attentive conditions. This appeared at around 30 ms and lasted up to 65 ms. This finding was consistent in the other recording channels. 4. Discussion
Fig. 3. Time–frequency representation of channel 6 at the left parietal scalp (obtained from the same subject as in Fig. 1). Upper panel: attentive session. Lower panel: neglect session. Ordinate – frequency; abscissa – time after the onset of stimuli. Note that relative signal energy powers at around 350 Hz are dominant between 30 and 55 ms in the attentive condition.
On the other hand, the signal strengths of HF activities following HFOs overlying N20–P20 components were increased under attentive conditions compared to neglect conditions. Although there was inter-individual variety, the frequency range of the HF activities was between 300 and 450 Hz and the appearance time was between 30 and 60 ms. For statistical analysis of attention-related HF activities, we therefore focused on 300–450 Hz frequencies and the period between 30 and 60 ms. We then obtained the maximal power from 7 subjects to be analyzed. Three-way (channel × subject × condition) ANOVA showed a significant main effect for subject and condition: for subject, F (6, 42) = 85.879, p = 5.74 × 10−22 and for condition, F (1, 42) = 172.001, p = 1.92 × 10−16. For the interaction term, the subject and the condition interaction term (F (6, 42) = 7.972, p = 9.04 × 10−6) alone was found significant. By analyzing the maximal signals of HF activities under attentive conditions for all 7 subjects, we obtained the characteristics of attention-related HF activity: a mean frequency of 355 Hz with 95% confidence interval between 335 and 376 Hz
We have found that, following HFOs overlying the N20–P20 component, HF activity at approximately 350 Hz appeared in human SEPs. Although there was inter-individual variation, the HF activity changed significantly due to the conditions; in other words, attention augments HF activity. This attention-related HF activity was found at 35–45 ms after stimulation and lasted up to 60 ms. HF activity following N20–P20 or N20m has attracted little attention in research into SEPs or somatosensory evoked fields (SEFs). However, in a recent SEP study on cortical HFOs by Restuccia’s
Fig. 4. Time–frequency representation of channel 2 at the left fronto-central scalp (obtained from the grand averaged data). Upper panel: attentive session. Lower panel: neglect session. Ordinate – frequency; abscissa – time after the onset of stimuli. Note that relative signal energy powers at around 350 Hz are dominant between 30 and 65 ms in the attentive condition.
60 group, they showed HF activity after 30 ms (see Fig. 1 in Restuccia et al., 2003). In SEF research by Haueisen et al. (2001), HF activity after 30 ms was also discerned in their Fig. 2. We therefore believe that HF activity after 30 ms appears consistently in SEP or SEF recordings. Significant inter-subject variation in the appearance time suggests that HF activity after 30 ms in SEPs or SEFs reflects responses not directly evoked by sensory stimuli but induced by neural circuit activity. Recent animal experiments showed that HF activity (so-called ripples) at 80–200 Hz overlies spontaneous EEG in the cat brain (Grenier et al., 2001) as well as SEPs in the rat cortex (Jones and Barth, 1999). In a study on the correlation of neural activity with HF activity, Grenier et al. (2001) found that the firing pattern of the first rhythmic burst (FRB) cell is correlated with HF activity (ripples) recorded from the cortical surface of area 7 of the cat brain. The FRB cell is known to give rise to highfrequency (300–600 Hz) spike bursts recurring at fast rates such as 30–50 Hz (Steriade, 2001). We therefore suppose that HF activity after 30 ms recorded in SEPs can be analogous to the ripples in the cat brain. The mechanism underlying augmentation of HF activity during attention is undetermined as yet. By analyzing single trial EEG during auditory attentive tasks, Winterer et al. (1999) found that EEG periods with fast reaction time performance are accompanied with a large amplitude of N100 response, and that there is an inverse relationship between the reaction time and noise power obtained by subtracting the averaged response from accumulated EEG strength. This suggests that a high vigilance level or good performance is correlated with a large amplitude evoked response and is accompanied by high levels of brain noise. Winterer et al. (1999) proposed a theory of stochastic resonance to explain this paradoxical phenomenon. When a specific input signal at or near the threshold level reaches the sensory system of a neural network, signals that are irrelevant to the specific sensory signal will augment output signals of the sensory system as stochastic resonance. In other words, a particular noise level can induce a multistable, excitable system to respond to a weak input signal. We therefore suppose that attention-related
HF activity may reflect this augmentation of neural noise, resulting in the facilitation of sensory information processing in a neural network as stochastic resonance. 5. Acknowledgement This research was partly supported by a Grant for Special Research Project, Aomori University of Health and Welfare. References Curio, G., Mackert, B.-M., Burghoff, M., Koetitz, R., AbrahamFuchs, K. and Härer, W. (1994) Localization of evoked neuromagnetic 600 Hz activity in the cerebral somatosensory system. Electroencephalogr. Clin. Neurophysiol., 91: 483–487. Grenier, F., Timofeev, I. and Steriade, M. (2001) Focal synchronization of ripples (80–200 Hz) in neocortex and their neuronal correlates. J. Neurophysiol., 86: 1884–1898. Hashimoto, I., Mashiko, T. and Imada, T. (1996) Somatic evoked high-frequency magnetic oscillations reflect activity of inhibitory interneurons in the human somatosensory cortex. Electroencephalogr. Clin. Neurophysiol., 100: 189–203. Haueisen, J., Schack, B., Meier, T., Curio, G. and Okada, Y. (2001) Multiplicity in the high-frequency signals during the shortlatency somatosensory evoked cortical activity in humans. Clin. Neurophysiol., 112: 1316–1325. Jones, M.S. and Barth, D.S. (1999) Spatiotemporal organization of fast (>200 Hz) electrical oscillations in rat vibrissa/barrel cortex. J. Neurophysiol., 82: 1599–1609. Ozaki, I., Suzuki, C., Yaegashi, Y., Baba, M., Matsunaga, M. and Hashimoto, I. (1998) High frequency oscillations in early cortical somatosensory evoked potentials. Electroencephalogr. Clin. Neurophysiol., 108: 536–542. Restuccia, D., Della Marca, G., Valeriani, M., Rubino, M., Paciello, N., Vollono, C., Capuano, A. and Tonali, P. (2003) Influence of cholinergic circuitries in generation of high-frequency somatosensory evoked potentials. Clin. Neurophysiol., 114: 1538–1548. Steriade, M. (2001) Impact of network activities on neuronal properties in corticothalamic systems. J. Neurophysiol., 86: 1–39. Ulrych, T.J. (1972) Maximum entropy power spectrum of truncated sinusoids. J. Geophys. Res., 77: 1396–1400. Winterer, G., Ziller, M., Dorn, H., Frick, K., Mulert, C., Dahhan, N., Herrmann, W.M. and Coppola, R. (1999) Cortical activation, signal-to-noise ratio and stochastic resonance during information processing in man. Clin. Neurophysiol., 110: 1193–1203. Yamada, T., Kameyama, S., Fuchigami, Y., Nakazumi, Y., Dickins, Q.S. and Kimura, J. (1988) Changes of short latency somatosensory evoked potential in sleep. Electroencephalogr. Clin. Neurophysiol., 70: 126–136.
57
Chapter 8
High-frequency oscillatory activities during selective attention in humans Isamu Ozakia,*, Yukoh Yaegashib, Masayuki Babac and Isao Hashimotod a
Faculty of Health Sciences, Aomori University of Health and Welfare, Aomori (Japan) b Department of Social Science, Akita Keijoh College, Oodate (Japan) c Department of Neurological Sciences, Hirosaki University School of Medicine, Hirosaki (Japan) d Human Information Systems Laboratory, Kanazawa Institute of Technology, Tokyo (Japan)
1. Introduction
2. Subjects and methods
The frequency of spontaneous electroencephalogram (EEG) is known to change with the level of vigilance: the low-frequency alpha wave is dominant at rest and the high-frequency gamma oscillation (mainly 30–40 Hz) appears at a high vigilance level. However, little attention has been given to changes in very high-frequency (>100 Hz) activities. Recent studies on somatosensory evoked magnetic fields or potentials disclosed that the initial cortical response contains a high-frequency (HF) component at around 600 Hz (Curio et al., 1994; Hashimoto et al., 1996; Ozaki et al., 1998), and the power of HF component changes during a sleep–waking cycle (Yamada et al., 1988; Hashimoto et al., 1996); however, there have been few reports on HF component changes during attentive tasks. We therefore tested whether EEG activities at extreme HF ranges alter in selective attention to stimuli using somatosensory evoked potentials (SEPs).
Seven normal adults (3 men, 4 women) participated in the experiments. The mean age was 29 years (range 20–45 years). The subject sat relaxed in a comfortable reclining chair in a quiet, air-conditioned, and electrically shielded room. During the recording session, the subject was encouraged to minimize muscle and eye blink interference and was kept awake. All the subjects gave their informed consent. Brief electric shock (0.2 ms duration) was applied to the right median nerve at the wrist at a rate of 5 stimuli/s. Intensity was adjusted so as to induce a small muscular twitch in the thenar muscles. SEPs were recorded from the frontal scalp at Fz (International 10–20 system), the ipsilateral central scalp at C4, and six electrodes covering the left centro-parietal scalp at FC3 (3 cm anterior to C3), C3, CP3 (3 cm posterior to C3), and 2 cm medial to FC3, C3, or CP3 (see Fig. 1). The right ear served as a reference. SEPs of 100 ms (5 ms before stimuli) were digitized at a 20 kHz sampling rate. In attentive sessions, we omitted the stimuli in a random sequence, and the subject was instructed to count omitting stimuli in their head. In neglect sessions, the subject listened to their favorite music to try to ignore the electric stimuli. For each run, about 1000 trials
*Correspondence to: Dr. Isamu Ozaki, Faculty of Health Sciences, Aomori University of Health and Welfare, 58-1 Mase, Hamadate, Aomori 030-8505, Japan. Tel/Fax: +81-17-765-2070; E-mail: [email protected]
58
Fig. 1. The position of the 8 electrodes over the scalp. Channel 7 (Ch 7): Fz; Ch 1: 3 cm anterior and 2 cm medial to C3; Ch 2: 3 cm anterior to C3; Ch 3: 2 cm medial to C3; Ch 4: C3; Ch 5: 3 cm posterior and 2 cm medial to C3; Ch 6: 3 cm posterior to C3; Ch 8: C4.
were averaged with a Dantec Keypoint electromyograph. Data were stored on floppy disks and converted to IBM format for later off-line analysis. We recorded 5–7 runs for attentive or neglect sessions. Then, grand averaging of attentive or neglect sessions was performed in each subject. The original filter setting was 0.5–2000 Hz. In off-line analysis, using a Hamming window, digital bandpass filtering of 300–1000 Hz was performed to separate high frequency oscillations (HFOs) from the underlying initial cortical response such as the N20 potential. Using a maximum entropy method (Ulrych, 1972), a power spectrum with a 10 ms period was calculated as the logarithm of power squared in base 10 for each frequency at every 1 ms interval. A time–frequency analysis of HF components was then obtained. For statistical analysis of HF components, we performed 3-way (channel × subject × condition) ANOVA. 3. Results Figure 2 shows SEP traces recorded from the left parietal scalp in a representative subject. There were no significant changes in the original wide-band traces between attentive and neglect conditions. However, for the post-filtered traces, HF activities at 300–400 Hz were dominant under attentive conditions. They appeared after 30 ms and lasted up to 60 ms under
Fig. 2. Effect of attention on SEPs to right median nerve stimulation in a 23-year-old woman. Top panel: the original SEP traces recorded from the left parietal scalp are compared between attentive and neglect conditions. The thick trace shows the attentive condition, and the thin trace, the neglect condition. Middle and bottom panels: the postfiltered traces of 8 channels for the attentive condition (middle) and neglect condition (bottom) are superimposed. Note that although HFOs overlying N20 seem unchanged, high-frequency activity at 300–400 Hz appears after 30 ms and lasts up to 60 ms in the attentive condition (small arrows).
attentive conditions, although HFOs overlying N20 seemed unchanged. Although the HF activities appeared at around 60 ms under neglect conditions, they did not last long compared with those under attentive conditions. In Fig. 3, the results of time–frequency analysis for the left parietal scalp recording in the same subject as in Fig. 1 are illustrated. HF activity at approximately 350 Hz was discerned under attentive conditions. HF activity also appeared at around 30 ms and lasted up to 55 ms in other recording channels under attentive conditions. For HFOs overlying N20–P20 and central P22 potentials, there were no obvious changes between attentive and neglect conditions in the subjects examined.
59 and a mean appearance time of 40 ms with 95% confidence interval between 35 and 45 ms. Figure 4 shows the time–frequency analysis of the left fronto-central scalp recording of the grand averaged data for 7 subjects. The dominant HF activity at around 350 Hz was discerned under attentive conditions. This appeared at around 30 ms and lasted up to 65 ms. This finding was consistent in the other recording channels. 4. Discussion
Fig. 3. Time–frequency representation of channel 6 at the left parietal scalp (obtained from the same subject as in Fig. 1). Upper panel: attentive session. Lower panel: neglect session. Ordinate – frequency; abscissa – time after the onset of stimuli. Note that relative signal energy powers at around 350 Hz are dominant between 30 and 55 ms in the attentive condition.
On the other hand, the signal strengths of HF activities following HFOs overlying N20–P20 components were increased under attentive conditions compared to neglect conditions. Although there was inter-individual variety, the frequency range of the HF activities was between 300 and 450 Hz and the appearance time was between 30 and 60 ms. For statistical analysis of attention-related HF activities, we therefore focused on 300–450 Hz frequencies and the period between 30 and 60 ms. We then obtained the maximal power from 7 subjects to be analyzed. Three-way (channel × subject × condition) ANOVA showed a significant main effect for subject and condition: for subject, F (6, 42) = 85.879, p = 5.74 × 10−22 and for condition, F (1, 42) = 172.001, p = 1.92 × 10−16. For the interaction term, the subject and the condition interaction term (F (6, 42) = 7.972, p = 9.04 × 10−6) alone was found significant. By analyzing the maximal signals of HF activities under attentive conditions for all 7 subjects, we obtained the characteristics of attention-related HF activity: a mean frequency of 355 Hz with 95% confidence interval between 335 and 376 Hz
We have found that, following HFOs overlying the N20–P20 component, HF activity at approximately 350 Hz appeared in human SEPs. Although there was inter-individual variation, the HF activity changed significantly due to the conditions; in other words, attention augments HF activity. This attention-related HF activity was found at 35–45 ms after stimulation and lasted up to 60 ms. HF activity following N20–P20 or N20m has attracted little attention in research into SEPs or somatosensory evoked fields (SEFs). However, in a recent SEP study on cortical HFOs by Restuccia’s
Fig. 4. Time–frequency representation of channel 2 at the left fronto-central scalp (obtained from the grand averaged data). Upper panel: attentive session. Lower panel: neglect session. Ordinate – frequency; abscissa – time after the onset of stimuli. Note that relative signal energy powers at around 350 Hz are dominant between 30 and 65 ms in the attentive condition.
60 group, they showed HF activity after 30 ms (see Fig. 1 in Restuccia et al., 2003). In SEF research by Haueisen et al. (2001), HF activity after 30 ms was also discerned in their Fig. 2. We therefore believe that HF activity after 30 ms appears consistently in SEP or SEF recordings. Significant inter-subject variation in the appearance time suggests that HF activity after 30 ms in SEPs or SEFs reflects responses not directly evoked by sensory stimuli but induced by neural circuit activity. Recent animal experiments showed that HF activity (so-called ripples) at 80–200 Hz overlies spontaneous EEG in the cat brain (Grenier et al., 2001) as well as SEPs in the rat cortex (Jones and Barth, 1999). In a study on the correlation of neural activity with HF activity, Grenier et al. (2001) found that the firing pattern of the first rhythmic burst (FRB) cell is correlated with HF activity (ripples) recorded from the cortical surface of area 7 of the cat brain. The FRB cell is known to give rise to highfrequency (300–600 Hz) spike bursts recurring at fast rates such as 30–50 Hz (Steriade, 2001). We therefore suppose that HF activity after 30 ms recorded in SEPs can be analogous to the ripples in the cat brain. The mechanism underlying augmentation of HF activity during attention is undetermined as yet. By analyzing single trial EEG during auditory attentive tasks, Winterer et al. (1999) found that EEG periods with fast reaction time performance are accompanied with a large amplitude of N100 response, and that there is an inverse relationship between the reaction time and noise power obtained by subtracting the averaged response from accumulated EEG strength. This suggests that a high vigilance level or good performance is correlated with a large amplitude evoked response and is accompanied by high levels of brain noise. Winterer et al. (1999) proposed a theory of stochastic resonance to explain this paradoxical phenomenon. When a specific input signal at or near the threshold level reaches the sensory system of a neural network, signals that are irrelevant to the specific sensory signal will augment output signals of the sensory system as stochastic resonance. In other words, a particular noise level can induce a multistable, excitable system to respond to a weak input signal. We therefore suppose that attention-related
HF activity may reflect this augmentation of neural noise, resulting in the facilitation of sensory information processing in a neural network as stochastic resonance. 5. Acknowledgement This research was partly supported by a Grant for Special Research Project, Aomori University of Health and Welfare. References Curio, G., Mackert, B.-M., Burghoff, M., Koetitz, R., AbrahamFuchs, K. and Härer, W. (1994) Localization of evoked neuromagnetic 600 Hz activity in the cerebral somatosensory system. Electroencephalogr. Clin. Neurophysiol., 91: 483–487. Grenier, F., Timofeev, I. and Steriade, M. (2001) Focal synchronization of ripples (80–200 Hz) in neocortex and their neuronal correlates. J. Neurophysiol., 86: 1884–1898. Hashimoto, I., Mashiko, T. and Imada, T. (1996) Somatic evoked high-frequency magnetic oscillations reflect activity of inhibitory interneurons in the human somatosensory cortex. Electroencephalogr. Clin. Neurophysiol., 100: 189–203. Haueisen, J., Schack, B., Meier, T., Curio, G. and Okada, Y. (2001) Multiplicity in the high-frequency signals during the shortlatency somatosensory evoked cortical activity in humans. Clin. Neurophysiol., 112: 1316–1325. Jones, M.S. and Barth, D.S. (1999) Spatiotemporal organization of fast (>200 Hz) electrical oscillations in rat vibrissa/barrel cortex. J. Neurophysiol., 82: 1599–1609. Ozaki, I., Suzuki, C., Yaegashi, Y., Baba, M., Matsunaga, M. and Hashimoto, I. (1998) High frequency oscillations in early cortical somatosensory evoked potentials. Electroencephalogr. Clin. Neurophysiol., 108: 536–542. Restuccia, D., Della Marca, G., Valeriani, M., Rubino, M., Paciello, N., Vollono, C., Capuano, A. and Tonali, P. (2003) Influence of cholinergic circuitries in generation of high-frequency somatosensory evoked potentials. Clin. Neurophysiol., 114: 1538–1548. Steriade, M. (2001) Impact of network activities on neuronal properties in corticothalamic systems. J. Neurophysiol., 86: 1–39. Ulrych, T.J. (1972) Maximum entropy power spectrum of truncated sinusoids. J. Geophys. Res., 77: 1396–1400. Winterer, G., Ziller, M., Dorn, H., Frick, K., Mulert, C., Dahhan, N., Herrmann, W.M. and Coppola, R. (1999) Cortical activation, signal-to-noise ratio and stochastic resonance during information processing in man. Clin. Neurophysiol., 110: 1193–1203. Yamada, T., Kameyama, S., Fuchigami, Y., Nakazumi, Y., Dickins, Q.S. and Kimura, J. (1988) Changes of short latency somatosensory evoked potential in sleep. Electroencephalogr. Clin. Neurophysiol., 70: 126–136.