European Journal of Neuroscience

European Journal of Neuroscience, Vol. 35, pp. 826–837, 2012

doi:10.1111/j.1460-9568.2012.08006.x

NEUROSYSTEMS

Trial-to-trial correlation between thalamic sensory response and global EEG activity Yonatan Katz (

),1 Michael Okun1,2 and Ilan Lampl1

1

Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel Department of Bioengineering, Imperial College London, London, UK

2

Keywords: juxtacellular recordings, Sprague Dawley rats, VPM, whiskers

Abstract Thalamic gating of sensory inputs to the cortex varies with behavioral conditions, such as sleep–wake cycles, or with different stages of anesthesia. Behavioral conditions in turn are accompanied by stereotypic spectral content of the EEG. In the rodent somatosensory system, the receptive field size of the ventral posteromedial thalamic nucleus (VPM) shrinks when anesthesia is deepened. Here we examined whether evoked thalamic responses are correlated with global EEG activity on a fine time scale of a few seconds. Trial-by-trial analysis of responses of VPM cells to whisker stimulation in lightly anesthetized rats indicated that increased EEG power in the delta band (1–4 Hz) was accompanied by a small, but highly significant, reduction in spontaneous and evoked thalamic firing. The opposite effect was found for the gamma EEG band (30–50 Hz). These significant correlations were not accompanied by an apparent change in the size of the receptive fields and were not EEG phase-related. The correlation between EEG and firing rate was observed only in neurons that responded to multiple whiskers and was higher for the non-principal whiskers. Importantly, the contributions of the two EEG bands to the modulation of VPM responses were to a large extent independent of each other. Our findings suggest that information conveyed by different whiskers can be rapidly modulated according to the global brain activity.

Introduction The EEG is a measure of global electrical brain activity and reflects changes in behavioral states of humans and animals. Unit recordings have shown that responses of cortical neurons to sensory stimulation are correlated with EEG activity. In particular, higher rates and more tonic firing occur when the EEG becomes less synchronized, i.e., characterized by higher frequencies (Steriade et al., 1990). EEG spectral power analysis is commonly used to identify transitions between different brain states (Hobson & Pace-Schott, 2002). Increased gamma activity indicates, for example, a more wakeful state (Cantero et al., 2004). Thalamic firing is also correlated with different behavioral states (Harmony et al., 1996; Steriade, 2000), suggesting that at least part of the correlation between EEG and the responses of single cortical neurons to sensory stimulation originates in the thalamus. Altered patterns of thalamic activity coupled with large changes in EEG activity are associated with distinct behavioral conditions, such as during wake–sleep cycles (Steriade & Contreras, 1995). Correlation of thalamic activity and global EEG activity is found across different sensory modalities, such as the auditory (Edeline et al., 2000), visual (Worgotter et al., 1998) and somatosensory (Friedberg et al., 1999) systems.

Correspondence: Yonatan Katz, as above. E-mail: [email protected] Received 24 June 2011, accepted 16 December 2011

A major pathway of the vibrissae system that conveys tactile information from the whiskers of rodents to the barrel cortex is the lemniscal pathway that passes via the ventral posteromedial nucleus of the thalamus (VPM). Neurons in this system usually respond vigorously to stimulation of one whisker, called the principal whisker (PW), and more weakly to its surround whiskers (SWs). Together they constitute the receptive field (RF) of the neuron. The RF size of thalamic neurons is affected by different physiological conditions, such as injury (Chu et al., 2004) or the level of anesthesia. Under deep anesthesia both the response and the RF size of VPM neurons decrease (Armstrong-James & Callahan, 1991; Friedberg et al., 1999, 2004). However, the relation between evoked thalamic response and global brain activities, probed by the EEG recording, has been studied, mostly across distinct brain states separated by prolonged time intervals (dozens of seconds at the very least). Here, we report that second-to-second variations in thalamic activity are significantly correlated with EEG power in specific frequency bands. In particular, we found that the correlation between EEG power and thalamic response is restricted to neurons that respond to multiple whiskers. For these cells, the highest correlations were found for responses evoked by stimulation of the SWs. Our results support the notion that the brain states modulate the response of thalamic cells and control the profile of their RF on a fine time scale. Such modulation may play an important role during rapid changes in animal behavior (Hirata et al., 2006; Stoelzel et al., 2009).

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd

Thalamic response and EEG 827

Materials and methods Animal preparation All surgical and experimental procedures were approved by the Weizmann Institute’s Animal Care and Use Committee. Sixty-four adult female Sprague–Dawley rats (8–13 weeks old, 190–260 g) were initially anesthetized with ketamine and xylazine mixture (i.p., 100 and 10 mg ⁄ kg respectively; Fort Dodge Animal Health, Fort Dodge, IA, USA). The rats were initially placed in a standard stereotaxic device using ear bars. Lidocaine (2%) was applied to the pressure points and around the area of surgery. Body temperature was kept at 37.0 ± 0.1 C using a heating blanket and a rectal thermometer (TC1000; CWE, Ardmore, PA, USA). The skin over the skull was cut and the periosteum was removed. Then bregma and lambda were leveled, and three craniotomies (1.4 mm in diameter) for EEG and unit recordings were made. A metallic post was attached to the skull using liquid cyanoacrylate and JET acrylic (Lang Dental Mfg, Wheeling, IL, USA). To reduce pain, the ear-bars were removed and during the rest of the experiment the animal’s head was held by the head-post only. Anesthesia Animals were ventilated through a mask (n = 31) or were tracheotomized and machine-ventilated (n = 33) (Katz et al., 2006). Both groups were breathing oxygen-enriched air and were lightly anesthetized with halothane (0.5–0.8%). The depth of anesthesia was assessed by vibrissae movements, reflexes and EEG. Following the surgical procedures described above, anesthesia level was reduced until clear signs of vibrissae movements, which indicate anesthetic level III-2 or III-1 (Friedberg et al., 1999), were observed. Usually when animals were whisking, the pinch withdrawal, eyelid and corneal reflexes could be easily evoked, indicating that the anesthesia level was not deep. Blood tests for pH levels of the self-breathing and machine-ventilated groups were within the normal physiological range (7.39 ± 0.02 and 7.43 ± 0.01, respectively; five animals from each group), indicating that the animals were well ventilated in both cases. For the analysis, we pooled the data that was obtained from the two groups. In nine animals the effect of deep anesthesia (1–1.5% halothane) was also tested. In order to reduce physiological effects of ketamine, recordings began at least 3 h after ketamine administration.

EEG and its analysis The EEG was recorded using two epidural PTFE-coated stainless steel wires with blunt tips (inner diameter 0.005¢¢; A-M Systems, Carlsborg, WA, USA), inserted into two craniotomies and glued to the skull with liquid cyanoacrylate. The locations of the two craniotomies were 4.0 and 6.0 mm caudal and 2.5 mm lateral to bregma. The EEG and VPM unit recordings were performed in contralateral hemispheres to minimize stimulus-driven EEG response. The signals were amplified ·5000, bandpass-filtered at 1–200 Hz and sampled at 3 kHz. The EEG power of each trial (2 s) was estimated from the absolute value of the fast Fourier transform. For comparison across experiments, the EEG power was normalized to the mean of all the trials recorded for a given cell. Five spectral bands were examined: 1–4, 4-8, 8–12, 12–25 and 30–50 Hz, corresponding to delta, theta, alpha, beta and gamma ranges, respectively (Steriade et al., 1993; Sauseng et al., 2008; Peng et al., 2010). To analyze the phase-coupling of thalamic spikes to the different bands of the EEG, we have used the Hilbert transform to extract the phases of EEG oscillations from the appropriately bandpass-filtered signals. The omnibus test was used to assess the uniformity of phase

distributions (Berens, 2009). In order to evaluate the phase-selectivity of cells, the phase-selectivity index (PSI) was calculated as follows (modified from Vogels & Orban, 1994):

PSI ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 Pn 2 i¼1 sin hi þ i¼1 cos hi n

;

where n is the number of spikes and hi is the phase of the Hilbert transform corresponding to the spike’s time.

Unit recordings Extracellular recordings were performed in the left VPM barreloid field using borosilicate micropipettes (outer diameter, 1.5 mm; inner diameter, 0.86 mm; Sutter Instruments, Novato, CA, USA), pulled with a P-97 micropipette puller (Sutter Instruments) to have a resistance of 15–40 MX (inner tip diameter < 1 lm). The signals were recorded using Axoclamp-2B (Molecular Devices, PaloAlto, CA, USA), followed by an additional amplification of ·1000 (MCP amplifier; Alpha-Omega Engineering, Nazareth, Israel), and low-pass filtered at 3 kHz. The signals were digitized at 10 kHz using a PCI6052E data acquisition board controlled by custom-written LabVIEW (Both from National Instruments, Austin, TX, USA) software. Electrodes were blindly advanced into the VPM. At the target depth of 4400–5300 lm, a test current pulse was injected through the electrode. Following an increase in electrode resistance and detection of spontaneous firing, the whisker pad was mechanically stimulated with an air puff to reveal specific responses. The small tip diameter of the recording electrodes provided a high signal-to-noise ratio, which enabled reliable identification of single units (Fig. 1B, C, E and F). The VPM neurons were identified by their brief and short-latency response. In several experiments (n = 8) the recording location was validated by electrical lesion (1 MX glass-isolated tungsten electrode, 0.5 mA for 1 s, 10 times in 50% duty cycle; Fig. 1A and D) performed at the same stereotaxic coordinates as the juxtacellular recording. Whisker stimulation and response quantification After identification of an air-puff-responsive VPM cell, the whiskers were deflected with a hand-held probe. The PW was defined as the whisker which had the highest response probability. If two whiskers had similar responses, the whisker that adapted less (i.e., had a smaller adaptation index; see below) was defined as the principal. During this stage the responsiveness to different directions of whisker deflection was evaluated by auditory feedback. Next, a selected whisker was inserted into a needle glued to a piezo-stimulator (T220-H4–203Y; Piezo Systems, Cambridge, MA, USA) and it was deflected in the preferred direction (additional details can be found in Katz et al., 2006). Mapping of a RF included 25 or 40 trials. Each trial consisted of five deflections of the whisker at 18 Hz, using the piezo-stimulator (reversed-ramp waveforms; rise and fall time were 3 and 20 ms, respectively; amplitude, 200 lm (1.1); angular velocity, 240 ⁄ s; inter-train interval, > 2 s). Mapping usually included the whiskers adjacent to the PW in the vertical and horizontal axes. If one of these whiskers was found to be responsive, its surrounding whiskers were also mapped. In some experiments (Fig. 1) the RF was initially mapped under light anesthesia and then again under deep anesthesia. We were careful to preserve the original position of the piezo stimulator when remapping the RFs for the second time. VPM neurons respond with high precision and have small temporal dispersion (Hartings et al., 2003), so RF maps were calculated from

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

828 Y. Katz et al. A

D

B

E

C

F

Fig. 1. Responses to whisker deflection of juxtacellulary recorded VPM neurons. (A) The location of the recording site in the VPM was verified by electrolytic lesion (marked by the arrow; see Materials and Methods). (B) Forty superimposed responses of a cell, in a recording site shown in A, demonstrate the high signal-tonoise ratio. The stimulation pattern of five deflections at 18 Hz is illustrated below. (C) Magnification of the responses to the first whisker deflection in B. VPM response is transient and has a low jitter. (D–F) Example of VPM recordings and lesion from a different animal. Format follows A–C.

20-ms post-stimulation intervals (the mean latency to peak response of all the recorded cells was 6.0 ms; data not shown). Peristimulus time histograms (PSTHs) were computed using 1-ms bins. The mean spontaneous firing rate was calculated for each whisker from the 200-ms intervals preceding the stimulation. These spikes and those which appeared 100 ms and more after the last whisker stimulation were used to analyze phase coupling of spontaneous firing with EEG activity. A whisker was considered to be a part of a neuron’s RF if its mean response in the 20-ms post-stimulation interval was higher than its spontaneous firing rate at a 99% confidence level (Kwegyir-Afful et al., 2005).

AI ¼

R1  R5 R1 þ R5

where R1 (R5) is the number of spikes elicited by the first (fifth) deflection. The AI values are between )1 and +1. AI is +1 for strongly adapting responses, 0 for steady responses and )1 for strongly facilitating responses.

Identification of tonic and burst spikes Adaptation During each trial a whisker was stimulated by a train of five deflections (18 Hz). The whisker’s adaptation index (AI) is defined as the ratio

Spikes were detected using a custom-written Matlab (The MathWorks, Natick, MA, USA) code. Burst was defined as a sequence of spikes with interspike interval (ISI) < 4 ms, following at least 100 ms of silence (Ramcharan et al., 2000; Wang et al., 2007).

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

Thalamic response and EEG 829 Histology In order to emphasize the VPM structure, cytochrome oxidase staining was used (Wong-Riley, 1979). To perform the staining, rats were over-anesthetized with halothane and perfused transcardially with 2.5% paraformaldehyde. The brain was removed and postfixed in the perfusion solution. After 24 h, the brain was immersed for an additional 24 h in paraformaldehyde solution with 25% sucrose for cryoprotection. Next, it was cut on a microtome (80-lm slices; SM 2000R; Leica, Heidelberg, Germany). Lesioned sections were washed twice in 0.1 M phosphate buffer at pH 7.4, each time for 10 min, and then incubated in oxygenated phosphate buffer solution containing 0.008% cytochrome c, 0.02% catalase and 0.05% 3,3¢-diaminobenzidine for 2–6 h, at 37 C. After appearance of the typical darkening in the VPM (Pierret et al., 2000), the sections were washed four times in PBS and mounted on gelatin-covered microscope slides. Mounted sections were air-dried, dehydrated in ethanol solution and xylene, and embedded in Entellan (ProSciTech, Thuringowa, Qld, Australia). The embedded sections were examined under a microscope (Eclipse E600; Nikon, Tokyo, Japan) and photomicrographs were taken with a digital camera (DXM1200F; Nikon).

Results The effect of anesthesia level on receptive field properties of VPM cells During our recordings the animals were lightly anesthetized (stage III-1 or III-2), unless stated otherwise. The level of anesthesia was

assessed according to accepted physiological signs (see Materials and methods). In these conditions 46% of the cells (41 ⁄ 89) responded to one whisker only, in contrast to a previous study in which VPM was found to contain only wide-RF cells that reduced their size from about six whiskers at stage III-2 of anesthesia to about two whiskers at stage III-4 (Friedberg et al., 1999). We examined the effect of deepening the anesthesia level on spontaneous and evoked firing of VPM cells in a subset of nine multiwhisker cells (see Materials and methods). Six of the nine cells were recorded in self-breathing rats and the other three in animals that were machine-respirated. Increasing the depth of anesthesia in self-breathing animals reduced the breathing rate from 2.17 ± 0.35 to 1.25 ± 0.23 Hz, suggesting a major change in the physiological condition of the animals. An example of an EEG recording in a lightly anesthetized animal (Fig. 2A; 0.5% halothane) demonstrates that EEG activity was substantially faster than under deeper anesthesia (Fig. 2D; 1% halothane, anesthesia stage III-3). The firing response of a thalamic cell that was recorded simultaneously with the EEG was substantially lower during deep anesthesia (Fig. 2B and E). Furthermore, its RF size decreased from five to three whiskers (Fig. 2C and F). On average across the nine cells, recorded from nine different animals, anesthesia had a similar effect on EEG activity, spontaneous rate, evoked firing rate and RF size (Fig. 2H–K). The average cumulative power spectrum of the EEG in these recordings indicates that under deep anesthesia (stage III-3), > 70% of the power was concentrated in frequencies < 5 Hz (Fig. 2G, black traces), compared to only 25% when the anesthesia was light (gray traces). Average spontaneous firing rate was reduced from 1 Hz to nearly zero under

A

B

C

D

E

F

G

H

I

J

K

Fig. 2. Altered EEG activity and reduced spontaneous and evoked firing rates of VPM cells under deeper anesthesia (A and B) An example of EEG activity and of a PSTH of VPM cell for PW (D1) deflections, recorded under light anesthesia. (C) The RF of the cell depicted in B. (D–F) For the same cell as in A–C, under deep anesthesia. (G) Normalized average cumulative power spectrum of the EEGs that were recorded during the mapping of the RF of nine cells (from nine different animals) under light (gray curves) and deep (black curves) anesthesia conditions. (H–K) Comparison of neuronal activities during deep and light anesthesia in the nine cells shows that deeper anesthesia caused: H, reduction in spontaneous activity; I, weaker evoked response; J, stronger adaptation; K, reduction in the size of the RF. ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

830 Y. Katz et al. deep anesthesia (Fig. 2H) and evoked response decreased from 0.81 ± 0.28 to 0.41 ± 0.29 spikes per stimulus (Fig. 2I). RF size was reduced from an average of 5.7 ± 2.1 whiskers under light anesthesia to 3.6 ± 1.7 whiskers under the deeper anesthesia (Fig. 2K). Similarly to previous studies (Khatri et al., 2004; Heiss et al., 2008), we found that VPM cells rapidly adapted to repetitive whisker stimulation. On average, the response probability in the lightly anesthetized animals dropped from 0.81 ± 0.27 spikes per stimulus for the first deflection to 0.40 ± 0.15 spikes per stimulus in the following deflections. In deeply anesthetized conditions the response probability dropped substantially more, from 0.41 ± 0.29 spikes per stimulus in the first stimulation to 0.06 ± 0.003 spikes per stimulus in the following deflections. As a result, the adaptation index (see Materials and methods) increased from 0.59 ± 0.19 to 0.91 ± 0.11 (P < 0.004, Fig. 2J). All these observations indicate that deep anesthesia has major effects on the firing properties of thalamic cells, and is accompanied by a shift towards lower frequencies in the EEG power spectrum.

Trial-to-trial variation in EEG activity The prominent changes in the activity of VPM cells caused by deepening the anesthesia level were accompanied by a substantial shift in the EEG power spectrum, which raises the question of correlation between the response of thalamic cells and the EEG activity on a time scale of seconds. However, before examining the correlation between EEG activity and evoked response on this time scale, we checked whether sensory stimulation affects EEG activity. This may happen, for example, if oscillatory activity in the cortex is entrained by the stimulus, as has previously been demonstrated in the visual cortex (Lakatos et al., 2008). We found that sensory stimulation had no effect on EEG activity measured in the hemisphere ipsilateral to the stimulated whiskers (contralaterally to its thalamic representation; Supporting Information Fig. S1). As whisker stimulation did not affect EEG activity, we proceeded to analyze the effect of rapid variations in EEG activity on thalamic responsiveness. We have examined on a trial-by-trial basis the relation between EEG and the properties of thalamic activity, such as spontaneous and sensory evoked firing rates (see Materials and methods). For this analysis we used recordings from lightly anesthetized animals, in which EEG power exhibited high trial-to-trial variability (Fig. 3B and D). Figure 3 shows two examples in which EEG was recorded while mapping the RF of the cells. The duration of each trial was 2 s (Fig. 3A and C; with inter-trial period of 50 ms). In the figure (Fig. 3B and D), the chronological order of the trials is grayscalecoded (dark traces preceded the bright ones). The lack of a clear relation between their order and the corresponding power values indicates that the variability in the power of the EEG did not reflect slow drift during the course of the experiment. This was quantified by calculating the power separately for each band in each trial and then computing the autocorrelation of these values. The autocorrelation drops to near zero values after one or two trials (Supporting Information Fig. S2), which cannot happen in the presence of a significant drift or correlation on a slow time scale.

Spontaneous thalamic firing and EEG activity To examine whether spontaneous thalamic firing was correlated with the rapid EEG activity fluctuations, we have calculated for each cell its average firing rate in the 40% of trials having the lowest EEG power and in the 40% of trials with the highest EEG power. By repeating this

A

B

C

D

Fig. 3. Rapid trial-to-trial EEG fluctuations in lightly anesthetized rats. EEG was recorded while mapping the RF of a VPM neuron. (A) Four randomly selected EEG traces (out of 40 repeats) recorded during single whisker stimulation. (B) Cumulative power spectrum for EEG traces recorded during whisker stimulation. The curves are grayscale-coded in their chronological order, with dark traces preceding the bright ones. Dashed numbered curves correspond to the EEG traces in A. (C and D) Another example from a different animal. Format follows A and B.

analysis for the different frequency bands, we found a significant correlation for some bands but not for others (Fig. 4A). For the low frequency bands (delta and theta), the average spontaneous firing rate was slightly but significantly higher in trials corresponding to the low EEG power (0.85 ± 0.03 vs. 0.74 ± 0.03 and 0.84 ± 0.03 vs. 0.72 ± 0.03 Hz, respectively; P < 0.0001). On the other hand, the spontaneous firing of thalamic cells was higher when gamma-band activity was elevated (0.76 ± 0.03 vs. 0.81 ± 0.03 Hz; P < 0.0001). During high delta EEG activity of slow-wave sleep, thalamic neurons emit more bursts than in the awake state (Steriade et al., 1993; Fanselow et al., 2001). Thus, we expected to find more bursts in trials of high delta power. Indeed, bursting was slightly more frequent (9 vs. 7.4% of the spikes) in trials with high delta EEG power, while the ISI within a burst was longer (2.98 ± 0.02 ms, n = 1026; and 2.91 ± 0.02 ms, n = 989): two-sample Kolmogorov–Smirnov test, P < 0.001; Fig. 4B). Because bursting activity of thalamic cells enhances the cortical response (Swadlow & Gusev, 2001), modulation of thalamic firing through changes in EEG activity may determine the activity of cortical cells.

Weak but significant trial-to-trial correlations between thalamic responses and power of particular EEG bands As altering the depth of anesthesia caused profound changes in the response and adaptation of thalamic cells to sensory stimulation as well as in the EEG activity (see Fig. 2A–G), we examined the instantaneous correlation between the power in different EEG frequency bands and the VPM response to repetitive stimulation of the whiskers when the light level of anesthesia was kept constant. Similarly to the analysis of the spontaneous activity, we have compared the 40% of trials with the lowest EEG power to the 40% with the highest power, for each whisker that was tested. Although the differences were small, significantly stronger responses were

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

Thalamic response and EEG 831 A

B

Fig. 4. Trial-to-trial correlation between the spontaneous thalamic firing and the EEG. (A) The average spontaneous firing rate was computed for two pooled groups of trials, containing the 40% of trials with the lowest and the 40% of trials with the highest EEG power for every cell, measured separately for each frequency band (gray and black, respectively). Significant differences are marked by asterisks. (B) Normalized cumulative distribution of the ISI within bursts, in trials with high (black) and low (gray) EEG power in the delta band. The distributions are significantly different (Kolmogorov–Smirnov test, P < 0.001).

measured when EEG power in the delta or the theta band was low (Fig. 5A and B). An opposite relation was found for the gamma band (Fig. 5E). No clear differences were found for the alpha and beta EEG bands (Fig. 5C and D). Furthermore, the correlation between delta or gamma band power and the response was by 50% higher for the second to the fifth deflections, compared to the first one. The stronger effect for subsequent stimuli suggests that a time-dependent process is involved in determining the influence of EEG on thalamic response (see Discussion). Despite the significant differences in response magnitude when EEG power increased in specific bands, we found no differences in receptive field size as defined by the number of whiskers that evoked a significant response (P > 0.05 for all bands; Wilcoxon rank-sum test).

The EEG-response correlation depended mostly on multi-whisker cells Because of the weaker response of surround whiskers to stimulation, they are more likely to be susceptible to modulation by fluctuations in brain activity. Therefore, we expected to observe a higher correlation with EEG activity for SWs than for the PW. To examine this point, we looked at the correlation strength separately in single- and multiwhisker cells. The whiskers driving the response in the latter were further subdivided into three groups: the PW, more responsive surround whiskers (four at most) and less responsive surround whiskers. Using the trial grouping procedure as above, we found no statistically significant correlation between the responsiveness and EEG activity in delta and gamma bands for single-whisker cells, whereas significant correlation was found for the PW of multi-whisker cells (Fig. 6). The response to stimulation of the less responsive SWs was almost 30% higher when delta activity was low and a slightly smaller effect was found when gamma activity was high. Hence, the significant correlation that we described above results from multiwhisker cells. Importantly, Fig. 6 shows that the weaker the response of a cell to stimulation of a given whisker, the stronger its correlation with both delta and gamma EEG activity.

The coupling between delta EEG activity and the response was predominantly independent of the coupling between gamma EEG and the response, and vice versa The thalamic response was negatively correlated with the power of EEG activity in the delta band and positively correlated with EEG power in the gamma frequency range. It is natural to ask whether these two results are independent of each other or are due to a co-variation in these two EEG bands. First, we examined the correlation between delta and gamma bands (Fig. 7A) in a pooled dataset, created by normalizing the EEG recordings in each experiment (see Materials and methods). A clear correlation between delta and gamma bands was found (R = )0.16, P < 10)9, Spearman’s rank correlation coefficient). However, the relation between them is not simple. For example, when the gamma values were examined as function of delta (circles) a positive correlation was evident for the low range of delta but this was negative for higher values of delta. To examine the extent to which the correlation of the thalamic response with gamma EEG power depends on its correlation with the delta band, we compared the correlation in the pooled dataset with shuffled data which preserves the joint distribution of the two EEG bands and of the delta band and the response, but destroys the trial-by-trial correlation between the gamma band and the response. To create such a surrogate distribution, the trials were sorted by their delta power values. Next, groups of consecutive sorted trials were binned together and the values of power in the gamma band within each bin were shuffled. A similar shuffling procedure was used to create surrogate distributions without the trial-by-trial correlations between delta EEG power and the response, while preserving the gamma–delta and gamma-response marginal distributions. In the surrogate distributions, the correlation of EEG power with the response was much lower than in the original data (0.03, )0.03 shuffled vs. 0.09, )0.06 original, for gamma and delta respectively), indicating that the correlations between thalamic response and EEG activity in these two bands were mostly independent (Fig. 7B and C).

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

832 Y. Katz et al. A

B

C

D

E

F

Fig. 5. The average response probability of VPM cells to a train of five stimuli, trials grouped by EEG power. (A–F) Each pair of bars represents the mean response to each stimulus within a train of five deflections, after grouping the trials by the corresponding EEG power (40% of the trials having the lowest and the highest power, gray and black, respectively). Asterisks mark significant differences (Student’s t-test, P < 0.05).

EEG phase coupling of VPM spikes The correlation between evoked thalamic response and EEG activity at fast time scales raises the possibility that the thalamic response is coupled to specific phases of EEG activity, similarly to the coupling that exists between cortical local field potential and unit firing. However, we found that the phases of the evoked thalamic responses (with respect to the EEG) displayed a nonuniform distribution for < 5% of the mapped whiskers (Omnibus test, P < 0.05) both in delta

(15 ⁄ 315 whiskers) and gamma (7 ⁄ 315 whiskers) bands. This suggests that there is no statistically significant evidence for modulation of whisker-evoked VPM responses by the phase of EEG oscillations, either in the delta or in the gamma band. On the other hand, in 23% of the cells the spontaneously emitted spikes had a nonuniform distribution with respect to the delta oscillations (Supporting Information Fig. S3). However, these cells showed low phase selectivity (phaseselectivity index 0.08 ± 0.08; see Materials and methods).

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

Thalamic response and EEG 833 A

A

B

B

Fig. 6. The correlation of the response with EEG was most prominent in multi-whisker cells and in particular for adjacent whiskers for which stimulation evoked a weak response. (A) The weaker the response of the cell to a stimulated whisker, the higher its correlation with EEG delta activity. The recordings were divided into four groups: single-whisker cells, PW of multiwhisker cells, four most responsive SWs, and responses from the less responsive SWs (5–9) of multi-whisker cells. In each group we compared the average response to single deflection in the 40% of trials with the highest (black) and lowest (gray) EEG power in the delta band. (B) As in A, for the gamma band. Asterisks mark significant differences (Student’s t-test, P < 0.05).

C

For gamma band oscillations, no statistically significant coupling with spontaneous spikes was observed.

Discussion Our results extend previous studies that examined how anesthesia affects the activity of VPM cells (Armstrong-James & George, 1988; Armstrong-James & Callahan, 1991; Friedberg et al., 1999, 2004; Castro-Alamancos, 2002). In the present study light anesthesia was maintained at a constant level and yet we found that evoked responses of VPM neurons varied from trial to trial, having a positive correlation with EEG power in the gamma band (30–50 Hz) and a negative correlation with the delta band (1–4 Hz). Although only a small proportion of the variability in VPM activity was explained by EEG, the correlations were highly significant. We also found that the correlation between the two EEG bands and the thalamic response is primarily observed in multi-whisker cells. Furthermore, the surround whiskers showed greater correlation with EEG power than did the principal whisker. For these, a 30% change in the response was found when trials were grouped based on the upper and lower 40th percentile of the EEG power. In spite of the above, RF size was not affected by EEG activity. Possibly, the changes in response profile across different whiskers were not large enough to unveil nonresponsive whiskers or to completely inactivate weak surround whiskers. Importantly, the correlations between the power in the two bands and the VPM responses were largely independent. No significant correlation between sensory evoked firing and EEG phase was found; however, in 20% of the cells the spontaneous firing showed statistically significant phase selectivity. We speculate that rapid modulation of the RF profile may allow thalamic cells to integrate information from a larger number of whiskers when animals are more alert (i.e., when delta activity is lower or when gamma is higher).

Fig. 7. The correlations between the response and either delta or gamma EEG bands were largely independent of each other. (A) The EEG power in the delta band was not independent of the power in the gamma band. Traces from all the experiments were divided into 10 bins according to the magnitude of the normalized power in the gamma ( ) or delta (O) band (every bin contained the same number of traces). Note that in the latter case the vertical axis serves as abscissa. The points represent the mean gamma and delta power in each of the 10 bins. (B) The correlation between gamma power and the response (R = 0.09, P < 10)9) is not explained by the delta response and gamma–delta distributions. This was verified by shuffling the gamma values so that the two latter marginal distributions were preserved, while the (noise) correlation between gamma power and the response was destroyed (R = 0.03). For visualization, the values were binned by the normalized power in the gamma band (¤, original data; h, shuffled traces). (C) The correlation between delta power and the response (R = )0.06, P < 10)9) is not explained by the gamma response and gamma–delta distributions (in the shuffled distribution R = )0.03). The graph is in the same format as B. In B and C the significance of the (remaining) correlation in the shuffled data was assessed to be < 0.05 by reshuffling multiple times.

Single- and multi-whisker RFs in the VPM The contraction of RF caused by deeper anesthesia supports previous results obtained in auditory (Edeline et al., 1999), visual (Worgotter et al., 1998; Li et al., 1999) and somatosensory (Armstrong-James & George, 1988) systems. Friedberg et al. (1999) demonstrated that under light anesthesia (III-1 and III-2) VPM cells exhibit only wide RFs, with an average size of about six whiskers. When they raised the anesthesia level the RF shrank and eventually, under deep anesthesia

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

834 Y. Katz et al. (stage III-4), the average size of the RF was only slightly larger than one whisker. By carefully monitoring the heart rate, whisking activity and eyelid, corneal and withdrawal reflexes, we determined that in our experimental conditions the anesthesia was maintained between stage III-1 and stage III-2, known as light conditions of anesthesia. In agreement with Friedberg et al. (1999), we found a significant reduction in RF size of multi-whisker cells when stage III-3 anesthesia was induced. However, in the light anesthesia condition and regardless of the variations in EEG activity, we found that 46% of the cells responded only to one whisker, while the average RF size was 2.5. A similar percentage of single-whisker cells was reported in an earlier study of lightly narcotized and sedated rats (Simons & Carvell, 1989). Even a smaller RF size than we found is inferred from the spiking activity of intracellularly recorded VPM cells (Brecht & Sakmann, 2002). Another study in which the stage of anesthesia was similar to ours, although maintained using ketamine and xylazine, reported RF size of about three whiskers (Timofeeva et al., 2004). However, compared to this work of Timofeeva et al., in our study the distribution of RF size was profoundly skewed towards single-whisker cells. What could be the possible explanation for the differences in RF size across these studies? Information ascends to the cortex via at least four different pathways in distinct regions of the VPM; two of them have only been reported in recent years (Pierret et al., 2000; Urbain & Deschenes, 2007). Neurons in these distinct VPM regions appear to have very different RF sizes. In the ventrolateral and dorsomedial regions of the barreloid, neurons have multi-whisker RFs, whereas in the core of each barreloid RFs are mostly limited to a single whisker (Urbain & Deschenes, 2007; Bokor et al., 2008). In their study (Urbain & Deschenes, 2007), cells which were encountered first as the electrode was lowered (in the dorsomedial VPM) had multi-whisker fields whereas single-whisker cells were found deeper, in the core part of this structure. Hence, it is possible that we sampled a different population of VPM cells compared to the other studies. However, we failed to find a clear spatial organization of RF size across our recordings (Supporting Information Fig. S4) as suggested in (Urbain & Deschenes, 2007). In our experiments, recording position was determined relative to stereotaxic coordinates whereas in Urbain & Deschenes (2007) position was measured relative to the reconstructed barreloid. Hence, it is possible that we lost the accuracy required to distinguish between sub-regions of the VPM. In addition to reduced RF size when anesthesia was deeper, we found that spontaneous firing rate in the VPM was also reduced, in agreement with previous studies (Coenen & Vendrik, 1972; Li et al., 1999). Reduced firing rate of thalamic cells accompanied by a slower EEG activity has also been reported in the non-alert brain state in awake rabbits (Stoelzel et al., 2009).

Correlation between EEG and response properties of VPM cells In contrast to entrainment of EEG activity by prolonged exposure to visual stimulation (Lakatos et al., 2008), whisker stimulation did not entrain the EEG activity (Supporting Information Fig. S1). Hence, the correlations between thalamic response and EEG activity were not driven by sensory stimulation. Correlation between EEG activity and evoked responses of single neurons is a prominent phenomenon which has been demonstrated across species and modalities (Munk et al., 1996; Friedberg et al., 1999; Massaux & Edeline, 2003; Fries et al., 2008) and under different conditions, such as deep and light anesthesia and also in different alertness states. For example, in awake rabbits spontaneous changes in attention state are characterized by an abrupt shift in

thalamic responses and the EEG activity (Bezdudnaya et al., 2006). The coupling between EEG and thalamic activities may be mediated at various levels from the brainstem to the cortex, via feedforward and feedback inputs, respectively. The similarity between the effects of altering the depth of anesthesia and those observed during spontaneous EEG fluctuations in lightly anesthetized rats suggests that they may share similar mechanisms. Modulation of thalamic circuits at different arousal states may affect thalamic response and RF size (Friedberg et al., 1999; Aguilar & Castro-Alamancos, 2005; Hirata et al., 2006) by different mechanisms. Corticothalamic feedback projections are one possible source of this modulation. These inputs alter the profile of thalamic RF in the visual thalamus (Andolina et al., 2007). In the somatosensory thalamus, (Temereanca & Simons, 2004) activation of a specific cortical column enhances the whisker-evoked responses of aligned thalamic cells whereas the responses are suppressed by activation of an adjacent cortical column. The differential correlation of SW responses and EEG power that we found in our study is in agreement with this source of modulation. The possible role of feedback inputs in explaining our findings is supported by our observation that the effect of EEG activity on the response to the first deflection is smaller than its effect on the subsequent stimuli in the train of deflections (Fig. 5). The suppression of thalamic responses is likely to be mediated by alteration of feedback inhibition from the reticular nucleus of the thalamus (Castro-Alamancos, 2002; Aguilar & Castro-Alamancos, 2005). However, RFs of brainstem inputs from the principal trigeminal nucleus (PrV) may be affected by arousal state as well. Indeed, earlier studies conducted in deeply anesthetized rats showed that most of the PrV neurons are single-whisker cells (Jacquin et al., 1988; Veinante & Deschenes, 1999), while RFs of PrV neurons in lightly anesthetized rats were found to be larger (Minnery et al., 2003; Kwegyir-Afful et al., 2005). In the study of Friedberg et al. (1999) anesthesia-dependent changes in RF size of VPM neurons were eliminated following lesion in the interpolaris brainstem nucleus (SpVi), suggesting that inputs from the SpVi are also dependent on the level of anesthesia. The role of brainstem circuits in controlling the RF size in the somatosensory system and the lack of a homologous structure in the visual system could explain why the RF size of neurons in the cat or rabbit LGN is insensitive to changes in EEG activity (Livingstone & Hubel, 1981; Bezdudnaya et al., 2006). Our results indicate that activity evoked by stimulation of SWs is more correlated with delta and gamma bands than is the response to PW. Because stimulation of a surround whisker results in a weaker response, we hypothesize that it is more susceptible to small fluctuations in baseline membrane potential, caused by changes in brain state. Despite significant correlations between EEG and thalamic response, we found that RF size was not correlated with EEG activity, probably because changes in thalamic responses were too small to uncover nonresponsive whiskers. By examining how thalamic response depends on EEG power in the different bands our results contribute to the understanding of the correlations between different brain rhythms. Because the level of both spontaneous and evoked thalamic firing was higher in trials with low delta power and in trials of high gamma power, and because of the significant negative correlation between delta and gamma activities, we expected some interdependency between the two correlations. In other words, trials in which delta EEG was low and in which firing activity was elevated might also be accompanied by increased gamma activity. However, de facto this was not the prevalent case (see Fig. 7). The correlation of the spiking activity with either gamma or delta power was found to be only slightly dependent on the other band, meaning that elevated thalamic firing during strong gamma EEG is not coupled to low delta EEG activity. Hence, several independent global mechanisms may

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

Thalamic response and EEG 835 control the response of thalamic cells. In more detail, the generator of the delta EEG waves is believed to depend on the activity of pyramidal cortical cells (Steriade et al., 1990), while gamma waves are thought to be generated by the thalamocortical loop (Steriade et al., 1996; Sukov & Barth, 2001) and the hippocampus (Bragin et al., 1995). Other studies support the view that gamma activity results from synchronized activity of inhibitory cortical cells (Whittington et al., 1995; Traub et al., 1996; Cardin et al., 2009). Some coupling between gamma and delta power has been reported in humans (Bruns & Eckhorn, 2004; Knyazev et al., 2005), in agreement with the weak correlation between these two bands that we have found in this study. However, inspection of the interdependence of these two bands (Fig. 7A) suggests that the coupling between them is complex. When gamma power was relatively low, delta activity was negatively correlated with gamma, whereas for high values of gamma a positive correlation was found.

Modulation of thalamic response by EEG activity does not happen with millisecond precision In some studies the comparison of cortical responses has been made between states lying many minutes apart and characterized by a substantially different amount of synchrony (Hirata & CastroAlamancos, 2011; Marguet & Harris, 2011). More relevant for the present work are studies which looked at how brain state fluctuations on a timescale of about a second affect sensory responses of cortical cells. These studies were conducted both in anesthetized animals, in particular during Up and Down cortical activity (Haider et al., 2006; Hasenstaub et al., 2007), and in awake animals (Poulet & Petersen, 2008). The present study shows that on this timescale the information transmitted to the cortex from the thalamic level is already modulated by the global brain state. Despite the significant correlation between thalamic response and the power of the global EEG activity, on a finer (phasic) time scale the evoked responses were not modulated by EEG activity in most cases, and only an insignificant number of whiskers had a phase-dependent response (< 5% both in delta and gamma bands). This might have been the case because the recording site of the EEG was contralateral to the representation of the stimulated whisker in the cortex and the thalamus. Alternatively, the significant second-to-second correlations and the absence of precise phasic relations between the thalamic response and the EEG may reflect modulatory mechanisms that act on a time scale of a second. These mechanisms, e.g., cholinergic inputs (Goard & Dan, 2009; Hirata & Castro-Alamancos, 2010), are insensitive on a millisecond time range. Lack of a millisecond resolution for modulation of thalamic response by EEG activity is also evident from the similarity between the latency of spikes for the high and low delta power trials (nonsignificant difference in latency was found in 98% of the whiskers, Wilcoxon rank-sum test, P > 0.05). Similar results were found in all other EEG bands. Could small variations in thalamic response exert a large effect on cortical response? At least two mechanisms can amplify small changes in the thalamic response when they are transmitted downstream to the cortex. First, fluctuations in thalamic synchrony (Wang et al., 2010) may lead to large modulation in cortical response. Second, small changes in thalamic input can be considerably amplified by the threshold effect in the recurrent cortical circuits. Although we did not include cortical recordings in this study, our results may explain a proportion of the large trial-to-trial variability and state-dependent sensory response of neurons of the somatosensory cortex (Petersen et al., 2003). In summary, we found that spontaneous and sensory evoked firing rates undergo rapid modulations on a time scale of seconds, which are

correlated with global EEG activity. Faster EEG activity as a result of reduced delta or increased gamma power is correlated with a larger response of second-order whiskers.

Supporting Information Additional supporting information can be found in the online version of this article: Fig. S1. The spectral powers of the EEG bands with or without sensory stimulation in the ipsilateral whisker pad were not significantly different. In order to quantify the effect of whisker stimulation on the EEG activity in the hemisphere ipsilateral to the stimulation, EEG was recorded during 80 trials; in a randomly selected half of the trials the whisker was stimulated. It was found that in each band the EEG power during whisker stimulation was not significantly different from trials without whisker stimulation (n = 21 whiskers from five animals). Fig. S2. Trial-to-trial variations of EEG are not due to a slow drift in brain activity. (A – left panel) Example vectors from three different animals corresponding to the delta power during 80 s of single whisker mapping. Each point in a vector reflects the delta power during a single 2 s trial, the power in each 2 s trial is changed from trial to trial and there is no slow drift in the power during the recording time. (A – middle panel) Normalized autocorrelation of the three example vectors in A. (A – right panel) Normalized autocorrelation of the whole population (gray) together with the randomized autocorrelation (cyan). Same scale as the middle panel. (B–F) Same conventions as in A but for different EEG bands (specified above the panels). The data from same three recording traces was used for all example plots. Fig. S3. Polar plots of spike-EEG phase relationship during spontaneous firing for all the cells that exhibit non-uniform phase distribution. During spontaneous activity 23% of the cells (21 ⁄ 89) had a non-uniform phase distribution (Omnibus test, P = 0.05). Note that these cells showed weak phase selectivity (PSI, see Methods) and that across the population there is no clear preferred phase. Fig. S4. Spatial distribution of multi and single whisker cells in the VPM. (A) Multi (gray) and single (black) whisker cells were plotted according to their spatial location with respect to Bregma. (B) The location of the neurons for each group was projected onto the line that connects the two means. We found no significant spatial segregation between the two groups (Kolmogorov–Smirnov, P = 0.086). Please note: As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer-reviewed and may be re-organized for online delivery, but are not copy-edited or typeset by Wiley-Blackwell. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.

Acknowledgements We would like to thank Marina Taran and Valerie Mazig for their help with the histological reconstructions. This work was supported by grant no. 326 ⁄ 07 from the Israel Science Foundation and by the Minerva Foundation of the Federal German Ministry for Education and Research and the Israeli Ministry of Science and Technology. Support was also provided by the Henry S. and Anne Reich Research Fund for Mental Health and the Asher and Jeanette Alhadeff Research Award.

Abbreviations ISI, interspike interval; PSTH, peristimulus time histogram; PW, principal whisker; RF, receptive field; SW, surround whisker; VPM, ventral posteromedial nucleus of the thalamus.

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

836 Y. Katz et al.

References Aguilar, J.R. & Castro-Alamancos, M.A. (2005) Spatiotemporal gating of sensory inputs in thalamus during quiescent and activated states. J. Neurosci., 25, 10990–11002. Andolina, I.M., Jones, H.E., Wang, W. & Sillito, A.M. (2007) Corticothalamic feedback enhances stimulus response precision in the visual system. Proc. Natl. Acad. Sci. USA, 104, 1685–1690. Armstrong-James, M. & Callahan, C.A. (1991) Thalamo-cortical processing of vibrissal information in the rat. II. spatiotemporal convergence in the thalamic ventroposterior medial nucleus (VPm) and its relevance to generation of receptive fields of S1 cortical ‘‘barrel’’ neurones. J. Comp. Neurol., 303, 211–224. Armstrong-James, M. & George, M.J. (1988) Influence of anesthesia on spontaneous activity and receptive field size of single units in rat Sm1 neocortex. Exp. Neurol., 99, 369–387. Berens, P. (2009) CircStat: a MATLAB toolbox for circular statistics. J. Stat. Softw., 31, 10. Bezdudnaya, T., Cano, M., Bereshpolova, Y., Stoelzel, C.R., Alonso, J.M. & Swadlow, H.A. (2006) Thalamic burst mode and inattention in the awake LGNd. Neuron, 49, 421–432. Bokor, H., Acsady, L. & Deschenes, M. (2008) Vibrissal responses of thalamic cells that project to the septal columns of the barrel cortex and to the second somatosensory area. J. Neurosci., 28, 5169–5177. Bragin, A., Jando, G., Nadasdy, Z., Hetke, J., Wise, K. & Buzsaki, G. (1995) Gamma (40–100 Hz) oscillation in the hippocampus of the behaving rat. J. Neurosci., 15, 47–60. Brecht, M. & Sakmann, B. (2002) Dynamic representation of whisker deflection by synaptic potentials in spiny stellate and pyramidal cells in the barrels and septa of layer 4 rat somatosensory cortex. J. Physiol., 543, 49–70. Bruns, A. & Eckhorn, R. (2004) Task-related coupling from high- to lowfrequency signals among visual cortical areas in human subdural recordings. Int. J. Psychophysiol., 51, 97–116. Cantero, J.L., Atienza, M., Madsen, J.R. & Stickgold, R. (2004) Gamma EEG dynamics in neocortex and hippocampus during human wakefulness and sleep. Neuroimage, 22, 1271–1280. Cardin, J.A., Carlen, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., Tsai, L.H. & Moore, C.I. (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature, 459, 663–667. Castro-Alamancos, M.A. (2002) Different temporal processing of sensory inputs in the rat thalamus during quiescent and information processing states in vivo. J. Physiol., 539, 567–578. Chu, K.L., Faltynek, C.R., Jarvis, M.F. & McGaraughty, S. (2004) Increased WDR spontaneous activity and receptive field size in rats following a neuropathic or inflammatory injury: implications for mechanical sensitivity. Neurosci. Lett., 372, 123–126. Coenen, A.M. & Vendrik, A.J. (1972) Determination of the transfer ratio of cat’s geniculate neurons through quasi-intracellular recordings and the relation with the level of alertness. Exp. Brain Res., 14, 227–242. Edeline, J.M., Manunta, Y., Nodal, F.R. & Bajo, V.M. (1999) Do auditory responses recorded from awake animals reflect the anatomical parcellation of the auditory thalamus? Hear. Res., 131, 135–152. Edeline, J.M., Manunta, Y. & Hennevin, E. (2000) Auditory thalamus neurons during sleep: changes in frequency selectivity, threshold, and receptive field size. J. Neurophysiol., 84, 934–952. Fanselow, E.E., Sameshima, K., Baccala, L.A. & Nicolelis, M.A. (2001) Thalamic bursting in rats during different awake behavioral states. Proc. Natl. Acad. Sci. USA, 98, 15330–15335. Friedberg, M.H., Lee, S.M. & Ebner, F.F. (1999) Modulation of receptive field properties of thalamic somatosensory neurons by the depth of anesthesia. J. Neurophysiol., 81, 2243–2252. Friedberg, M.H., Lee, S.M. & Ebner, F.F. (2004) The contribution of the principal and spinal trigeminal nuclei to the receptive field properties of thalamic VPM neurons in the rat. J. Neurocytol., 33, 75–85. Fries, P., Womelsdorf, T., Oostenveld, R. & Desimone, R. (2008) The effects of visual stimulation and selective visual attention on rhythmic neuronal synchronization in macaque area V4. J. Neurosci., 28, 4823–4835. Goard, M. & Dan, Y. (2009) Basal forebrain activation enhances cortical coding of natural scenes. Nat. Neurosci., 12, 1444–1449. Haider, B., Duque, A., Hasenstaub, A.R. & McCormick, D.A. (2006) Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J. Neurosci., 26, 4535–4545. Harmony, T., Fernandez, T., Silva, J., Bernal, J., Diaz-Comas, L., Reyes, A., Marosi, E. & Rodriguez, M. (1996) EEG delta activity: an indicator of attention to internal processing during performance of mental tasks. Int. J. Psychophysiol., 24, 161–171.

Hartings, J.A., Temereanca, S. & Simons, D.J. (2003) Processing of periodic whisker deflections by neurons in the ventroposterior medial and thalamic reticular nuclei. J. Neurophysiol., 90, 3087–3094. Hasenstaub, A., Sachdev, R.N. & McCormick, D.A. (2007) State changes rapidly modulate cortical neuronal responsiveness. J. Neurosci., 27, 9607– 9622. Heiss, J.E., Katz, Y., Ganmor, E. & Lampl, I. (2008) Shift in the balance between excitation and inhibition during sensory adaptation of S1 neurons. J. Neurosci., 28, 13320–13330. Hirata, A. & Castro-Alamancos, M.A. (2010) Neocortex network activation and deactivation states controlled by the thalamus. J. Neurophysiol., 103, 1147–1157. Hirata, A. & Castro-Alamancos, M.A. (2011) Effects of cortical activation on sensory responses in barrel cortex. J. Neurophysiol., 105, 1495–1505. Hirata, A., Aguilar, J. & Castro-Alamancos, M.A. (2006) Noradrenergic activation amplifies bottom-up and top-down signal-to-noise ratios in sensory thalamus. J. Neurosci., 26, 4426–4436. Hobson, J.A. & Pace-Schott, E.F. (2002) The cognitive neuroscience of sleep: neuronal systems, consciousness and learning. Nat. Rev. Neurosci., 3, 679–693. Jacquin, M.F., Golden, J. & Panneton, W.M. (1988) Structure and function of barrel ‘precursor’ cells in trigeminal nucleus principalis. Brain Res., 471, 309–314. Katz, Y., Heiss, J.E. & Lampl, I. (2006) Cross-whisker adaptation of neurons in the rat barrel cortex. J. Neurosci., 26, 13363–13372. Khatri, V., Hartings, J.A. & Simons, D.J. (2004) Adaptation in thalamic barreloid and cortical barrel neurons to periodic whisker deflections varying in frequency and velocity. J. Neurophysiol., 92, 3244–3254. Knyazev, G.G., Savostyanov, A.N. & Levin, E.A. (2005) Uncertainty, anxiety, and brain oscillations. Neurosci. Lett., 387, 121–125. Kwegyir-Afful, E.E., Bruno, R.M., Simons, D.J. & Keller, A. (2005) The role of thalamic inputs in surround receptive fields of barrel neurons. J. Neurosci., 25, 5926–5934. Lakatos, P., Karmos, G., Mehta, A.D., Ulbert, I. & Schroeder, C.E. (2008) Entrainment of neuronal oscillations as a mechanism of attentional selection. Science, 320, 110–113. Li, B., Funke, K., Worgotter, F. & Eysel, U.T. (1999) Correlated variations in EEG pattern and visual responsiveness of cat lateral geniculate relay cells. J. Physiol., 514(Pt 3), 857–874. Livingstone, M.S. & Hubel, D.H. (1981) Effects of sleep and arousal on the processing of visual information in the cat. Nature, 291, 554–561. Marguet, S.L. & Harris, K.D. (2011) State-dependent representation of amplitude-modulated noise stimuli in rat auditory cortex. J. Neurosci., 31, 6414–6420. Massaux, A. & Edeline, J.M. (2003) Bursts in the medial geniculate body: a comparison between anesthetized and unanesthetized states in guinea pig. Exp. Brain Res., 153, 573–578. Minnery, B.S., Bruno, R.M. & Simons, D.J. (2003) Response transformation and receptive-field synthesis in the lemniscal trigeminothalamic circuit. J. Neurophysiol., 90, 1556–1570. Munk, M.H., Roelfsema, P.R., Konig, P., Engel, A.K. & Singer, W. (1996) Role of reticular activation in the modulation of intracortical synchronization. Science, 272, 271–274. Peng, Y.Z., Li, X.X. & Wang, Y.W. (2010) Effects of Parecoxib and Fentanyl on nociception-induced cortical activity. Mol. Pain, 6, 3. Petersen, C.C., Hahn, T.T., Mehta, M., Grinvald, A. & Sakmann, B. (2003) Interaction of sensory responses with spontaneous depolarization in layer 2 ⁄ 3 barrel cortex. Proc. Natl. Acad. Sci. USA, 100, 13638–13643. Pierret, T., Lavallee, P. & Deschenes, M. (2000) Parallel streams for the relay of vibrissal information through thalamic barreloids. J. Neurosci., 20, 7455– 7462. Poulet, J.F. & Petersen, C.C. (2008) Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature, 454, 881–885. Ramcharan, E.J., Gnadt, J.W. & Sherman, S.M. (2000) Burst and tonic firing in thalamic cells of unanesthetized, behaving monkeys. Vis. Neurosci., 17, 55– 62. Sauseng, P., Klimesch, W., Gruber, W.R. & Birbaumer, N. (2008) Crossfrequency phase synchronization: a brain mechanism of memory matching and attention. Neuroimage, 40, 308–317. Simons, D.J. & Carvell, G.E. (1989) Thalamocortical response transformation in the rat vibrissa ⁄ barrel system. J. Neurophysiol., 61, 311–330. Steriade, M. (2000) Corticothalamic resonance, states of vigilance and mentation. Neuroscience, 101, 243–276. Steriade, M. & Contreras, D. (1995) Relations between cortical and thalamic cellular events during transition from sleep patterns to paroxysmal activity. J. Neurosci., 15, 623–642.

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837

Thalamic response and EEG 837 Steriade, M., Gloor, P., Llinas, R.R., Lopes de Silva, F.H. & Mesulam, M.M. (1990) Report of IFCN Committee on Basic Mechanisms. Basic mechanisms of cerebral rhythmic activities. Electroencephalogr. Clin. Neurophysiol., 76, 481–508. Steriade, M., McCormick, D.A. & Sejnowski, T.J. (1993) Thalamocortical oscillations in the sleeping and aroused brain. Science, 262, 679–685. Steriade, M., Contreras, D., Amzica, F. & Timofeev, I. (1996) Synchronization of fast (30–40 Hz) spontaneous oscillations in intrathalamic and thalamocortical networks. J. Neurosci., 16, 2788–2808. Stoelzel, C.R., Bereshpolova, Y. & Swadlow, H.A. (2009) Stability of thalamocortical synaptic transmission across awake brain states. J. Neurosci., 29, 6851–6859. Sukov, W. & Barth, D.S. (2001) Cellular mechanisms of thalamically evoked gamma oscillations in auditory cortex. J. Neurophysiol., 85, 1235–1245. Swadlow, H.A. & Gusev, A.G. (2001) The impact of ‘bursting’ thalamic impulses at a neocortical synapse. Nat. Neurosci., 4, 402–408. Temereanca, S. & Simons, D.J. (2004) Functional topography of corticothalamic feedback enhances thalamic spatial response tuning in the somatosensory whisker ⁄ barrel system. Neuron, 41, 639–651. Timofeeva, E., Lavallee, P., Arsenault, D. & Deschenes, M. (2004) Synthesis of multiwhisker-receptive fields in subcortical stations of the vibrissa system. J. Neurophysiol., 91, 1510–1515. Traub, R.D., Whittington, M.A., Colling, S.B., Buzsaki, G. & Jefferys, J.G. (1996) Analysis of gamma rhythms in the rat hippocampus in vitro and in vivo. J. Physiol., 493, 471–484.

Urbain, N. & Deschenes, M. (2007) A new thalamic pathway of vibrissal information modulated by the motor cortex. J. Neurosci., 27, 12407– 12412. Veinante, P. & Deschenes, M. (1999) Single- and multi-whisker channels in the ascending projections from the principal trigeminal nucleus in the rat. J. Neurosci., 19, 5085–5095. Vogels, R. & Orban, G.A. (1994) Activity of inferior temporal neurons during orientation discrimination with successively presented gratings. J. Neurophysiol., 71, 1428–1451. Wang, X., Wei, Y., Vaingankar, V., Wang, Q., Koepsell, K., Sommer, F.T. & Hirsch, J.A. (2007) Feedforward excitation and inhibition evoke dual modes of firing in the cat’s visual thalamus during naturalistic viewing. Neuron, 55, 465–478. Wang, Q., Webber, R.M. & Stanley, G.B. (2010) Thalamic synchrony and the adaptive gating of information flow to cortex. Nat. Neurosci., 13, 1534– 1541. Whittington, M.A., Traub, R.D. & Jefferys, J.G. (1995) Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation. Nature, 373, 612–615. Wong-Riley, M. (1979) Changes in the visual system of monocularly sutured or enucleated cats demonstrable with cytochrome oxidase histochemistry. Brain Res., 171, 11–28. Worgotter, F., Suder, K., Zhao, Y., Kerscher, N., Eysel, U.T. & Funke, K. (1998) State-dependent receptive-field restructuring in the visual cortex. Nature, 396, 165–168.

ª 2012 The Authors. European Journal of Neuroscience ª 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 35, 826–837