Heart rate variability is encoded in the spontaneous discharge of thalamic somatosensory neurones in cat

Keywords: 0748 Journal of Physiology (2000), 526.2,. 387 396 387 Heart rate variability is encoded in the sontaneous discharge of thalamic somatosensory neurones in cat Marcello Massimini, Alberto Porta *, Maurizio Mariotti, Alberto Malliani * and Nicola Montano * Laboratorio di Neurofisiologia and * Centro Ricerche Cardiovascolari CNR, Diartimento di Scienze Precliniche Laboratorio Interdiscilinare Tecnologie Avanzate di Vialba, Universita degli Studi di Milano, Italy, Osedale Luigi Sacco, via Giovan Battista Grassi 74, 20157 Milano, Italy (Received 23 February 2000; acceted after revision 28 Aril 2000) 1. We studied the sontaneous discharge variability of thalamocortical somatosensory neurones in the awake cat in order to disclose its ossible information content. The resence of slow (0 09 1 39 Hz) regular fluctuations in the discharge rate of these cells during the waking state has been reviously reorted. Oscillations in a similar frequency range are known to characterize the activity of central and eriheral neurones ertaining to the autonomic nervous system and the variability of heart eriod (RR interval variability). 2. A surrogate data test, erformed on our database, confirmed the resence of slow (0 05 1 Hz) non-random fluctuations in firing rate. 3. Linear regression detected the resence of an inverse relationshi between the values of RR interval and the concurrent levels of neural discharge. 4. Frequency domain analysis indicated that a significant couling between the two variability signals referentially occurred in two frequency bands: in the frequency of the resiratory sinus arrhythmia and in corresondence with a slower rhythm (0 07 0 3 Hz), the two signals being in hase oosition in most of the cases. 5. Coherent fluctuations could also be observed when eochs of evoked activity were analysed, while couling between the two variability signals aeared to be disruted after slee onset. 6. We conclude that RR interval variability, an internally generated dynamic related to basic visceral regulation, is encoded in the discharge of single somatosensory thalamocortical neurones during wakefulness. A ossible interaction with the transmission of somatosensory information has to be evaluated. The discharge roerties of thalamocortical somatosensory neurones are mainly studied in the awake animal, on the basis of a stimulus resonse rotocol. Using this aroach, the activity that is time locked to the actual stimulation of the secific eriheral recetor is extracted, while the rest of the discharge (i.e. the sontaneous discharge) cannot be interreted and some information, ossibly encoded, might not be evaluated. Werner & Mountcastle (1963) have reviously described a lack of stationarity in subsequent samles of sontaneous activity recorded from single ventrobasal neurones in the awake monkey. Non-stationarities were eliminated by shuffling the original data into random sequences. Deartures from randomness were found to be due to the resence of eriodic fluctuations in discharge rate, having a frequency ranging from 0 09 to 1 39 Hz. These slow eriodicities are unlikely to be related to intrinsic membrane roerties since, as revealed by numerous studies, thalamocortical cells, during wakefulness, fire tonically on the basis of a steadily deolarized membrane otential (Hirsch et al. 1983; Jahnsen & Llin as, 1984; Steriade & Deschḙnes, 1984; McCormick & Bal, 1997). Sontaneous eriodic changes in the discharge rate of these cells could be due to the effect of an inut different from the secific somatosensory one. The source of this inut should meet some rerequisites: its activity should be characterized by the resence of slow fluctuations in discharge rate and it should generate slow oscillatory atterns also during the waking state. Slow rhythmic fluctuations (< 1 Hz) characterize the firing attern of neurones ertaining to the autonomic nervous system. Lambertz & Langhorst (1998) detected the resence

388 M. Massimini, A. Porta, M. Mariotti, A. Malliani and N. Montano J. Physiol. 526.2 of slow oscillations in the imulse activity recorded from units located in the reticular formation and different brainstem nuclei. Such oscillations were coherent with those resent in the variability of efferent symathetic discharge and of cardiovascular signals such as heart eriod and arterial ressure. In view of this the reticular formation has been considered as art of a common brainstem system, rovided with ascending and descending rojections and integrating the regulation of somatic and visceral functions (Schulz et al. 1983, 1985; Langhorst et al. 1996). Fluctuations in discharge rate have been recorded in secific brainstem nuclei involved in cardiovascular regulation (Montano et al. 1996) and in the activity of symathetic fibres rojecting to the heart (Montano et al. 1992). Similar oscillations have been detected in muscle symathetic nerve activity recorded in awake human subjects (Pagani et al. 1997). These neural oscillations are coherent with the rhythms observable in the variability of heart eriod. In articular, sectral analysis of heart rate variability has been widely used as a tool to assess indirectly the state of symathetic and arasymathetic neural modulations to the sinus node (Malliani et al. 1991). The aim of our study was to assess whether the rate fluctuations detectable in thalamic somatosensory sontaneous discharge could be related to an oscillatory inut coming from structures ertaining to the autonomic nervous organization. Therefore, using linear regression and frequency domain analysis (via coherence and hase functions) we evaluated the ossible relationshis existing between heart rate (exressed as its recirocal the RR interval) and the discharge of single ventroosterior thalamocortical neurones in the awake cat. METHODS Surgical rocedure and signal recording Exeriments were carried out on four adult cats (2 8 4 2 kg). Animals were chronically imlanted. Care and handling of animals was in accordance with international guidelines (NIH) and aroved by the Animal Care Commitee of the University of Milan. In order to obtain head fixation without ressure or ain during the subsequent recording sessions, two nylon cylinders, fitted to the stereotaxic bars, were cemented to the skull while the animal was anaesthetized with ketamine (15 mg kg i.m.) followed by barbiturate anaesthesia (Somnotol 35 mg kg i..). Craniotomy was erformed in the right arietal skull, to allow a stereotaxic aroach to the ventroosterolateral (VPL) and the ventroosteromedial (VPM) thalamic nuclei. The hole was then temorarily closed with bone wax. Four screws were inserted in the frontal and arietal bone on both sides of the head for EEG recording and attached to a connector. Animals were allowed to recover for three weeks and were then habituated to the stereotaxic frame. During the recording sessions, tungsten microelectrodes (9 to 12 MÙ resistance) were lowered stereotaxically (A 8 5Ï9 8; L 3Ï7 8; H +3Ï 1 5) to reach and record thalamic units, extracellularly. Needles for ECG recording were inserted subcutaneously. Resiratory rate was measured using a strain-gauge transducer connected to a small mask that was adjusted and laced a few millimetres in front of the nostrils of the cat. This signal was not calibrated and reflected only the rate of resiration and not the resiratory activity. A brief uff of air on the animal s hair was used to stimulate the eriheral recetive field of the recorded neurone. Stimuli, with a mean duration of 15 ms and a eak ressure of 6 5 g cmâ, at a frequency of 1 5 Hz, were delivered on left limbs, neck and head. Electrohysiological identification of ventroosterior (VP) units was obtained by dislaying on-line ost-stimulus histograms (PSTH; Figure 1. Criteria for database selection A, tyical resonse of a ventroosterior thalamic neurone following 100 reeated stimulations of the hairy skin (bin width 1 ms). B, eri-trigger histogram on R wave of the QRS of ECG comlex erformed on 600 beats. Mean ECG (uer trace) and discharge distribution (bin width 1 ms; lower trace) do not show relationshis between neural firing and the heart beat.

J. Physiol. 526.2 VP sontaneous discharge and RR interval 389 1 ms bin) (Fig. 1A; Mariotti & Formenti, 1990). Both sontaneous and evoked neuronal activity were recorded during wakefulness and, in some cases, after slee onset. Activity of 10 to 15 min was recorded for each neurone. At the end of the exerimental cycle, animals were given a lethal dose of Nembutal (100 mg kg ). Signal rerocessing All signals were stored on tae and off-line AÏD converted with a samling frequency of 18 khz. Neural activity was then retrieved and visually insected; only neurones with a high and stable signalto-noise ratio were selected. Peri-trigger histograms on R wave of the electrocardiogram (ECG) were calculated for all neurones in order to detect the resence of heart beat related siking, which may reflect artifacts resulting from ulsatile movement (Fig. 1B). Neurones whose discharge was correlated to the heart beat were thus excluded from further analysis. Visual scoring of EEG and neural discharge, together with intersike interval histograms, were used to select eriods of tonic discharge low voltage EEG and eriods of burst discharge high voltage EEG. Time series extraction The ECG signal was comressed to 600 Hz, QRS comlexes were automatically detected using a threshold derivative algorithm and the time occurring between each R wave was calculated in order to obtain a time series of RR interval variability (tachogram). The resiratory signal was comressed to 600 Hz and samled once er cardiac beat. Sikes from VP were discriminated by means of a digital threshold and were counted on a 20 ms time basis roducing a ste-wise signal (the counted VP) with levels roortional to the number of detected sikes. The counted signal was comressed to 600 Hz and low-ass filtered at 1 Hz with a finite imulse resonse (FIR) filter (2400 coefficients, Hanning windowed). The filtered neuronal signal was then samled once er cardiac beat giving a beat-to-beat time series of neural discharge variability (neurogram) synchronous with the tachogram. The tachogram and the neurogram of each neurone were suerimosed and visually insected. Stable segments of about 250 beats were selected for further analysis. The criteria for stability were the absence of artifacts in the series due to error in R wave detection and the absence of transients due to sudden increases or decreases in neuronal firing or very slow trends in the mean values of RR interval and discharge rate. Series smaller than 250 samles did not allow reliable detection of oscillations with a frequency lower than 0 05 Hz (considering a maximum heart rate of 3 Hz). Data analysis Linear correlation. Linear regression in the lane (VP(i), RR(i)) was erformed to assess the tye and degree of correlation between the tachograms and neurograms. A direct relationshi (ositive changes in RR interval determine ositive variations in VP) was detected by a ositive sloe (a > 0). An inverse relationshi was found when a is negative. The strength of the relationshi was quantified by the correlation coefficient r. Signals were considered significantly correlated for P < 0 01. Power sectral density estimation Power sectral analysis was utilized to assess the ower and the frequency of the oscillations resent in the beat-to-beat variability signals of RR interval and VP. A beat-to-beat series x =(x(i)), where i is the rogressive cardiac beat, is described as an autoregressive (AR) rocess (Kay & Marle 1981): x(i) = Óa kx(i k) + e (i), (1) k =1 where ak are the coefficients of the model and e(i) is a white noise with zero mean and variance ëâ. The identification of the coefficients ak and of the variance ëâ of the white noise e(i) was erformed by Levinson-Durbin recursion (Kay & Marle, 1981) and the model order was chosen according to the Akaike figure of merit (Akaike, 1974). The ower sectral decomosition (PSD) rocedure (Baselli et al. 1997) allowed us to decomose the PSD in a sum of sectral eaks and to calculate ower and central frequency of these eaks. Surrogate data aroach In order to assess if the beat-to-beat neural discharge variability series is a coloured rocess (i.e. it is different from a white noise) a surrogate data test was erformed. The counted VP signal was shuffled by randomizing the levels reresenting the number of sikes found in 20 ms. Ten different surrogate realizations of the same original signal were obtained by changing the random sequence utilized to shuffle. The surrogate signals are rocessed like the original series to derive the beat-to-beat series of the VP discharge. The ower of the oscillations evaluated in the original series was comared with the ower found in the surrogate series. Rhythmicity in a given frequency band was considered significant if the ower in the original series was larger than the mean ower lus two times the standard deviation calculated in the surrogate data (Fig. 3) (Theiler et al. 1985). Phase relationshi and coherence function estimation The analysis of the relationshis between RR interval and VP was erformed via calculation of the hase relationshi and coherence function. The linear relationshi between two beat-to-beat variability series x =(x(i)) and y =(y(i)) are described by a bivariate AR rocess (Morf et al. 1985): x(i) = Óa11 kx(i k) + Óa12 ky(i k) + e 1(i), (2) k=1 k=1 y(i) = Óa21 kx(i k) + Óa22 ky(i k) + e 2(i), (3) k=1 k=1 where a11k, a12k, a21k, a22k are the coefficients of the bivariate AR rocess setting the influences of several ast values on the current one and the effects of one signal on the other one. The joint rocess v = xy can be seen as the outut of the 2 ² 2 transfer matrix H(z) with elements: A11(z) =1 Óa11kz k, A12(z) = Óa12 kz k,a21(z) = Óa21kz k, k =1 k =1 k =1 and A22(z) =1 Óa22 kz k. k =1 After identification of the coefficients of the bivariate model via the least squared method (Baselli et al. 1997), the cross-sectrum Sxy(f) was obtained as the non-diagonal terms of the matrix: S(f) =H(z)Ë 2 H'(z 1 ) z = ex(2ðjft), (4) where H'(z 1 ) is the transose of H(z 1 ) and Ë 2 is the variance matrix of the joint rocess e = e1 eµ. The hase relationshi is the

390 M. Massimini, A. Porta, M. Mariotti, A. Malliani and N. Montano J. Physiol. 526.2 hase of the cross-sectrum Sxy(f), where x leads for ositive hase values. The squared coherence function k Â(f) is obtained by normalizing the squared modulus of the cross-sectrum by the roduct of the two sectra Sx(f) and Sy(f). The coherence function ranges from 0 to 1 and measures the degree of linear correlation between two oscillations found in the two signals at the same frequency. A k Â(f) > 0 5 was considered significant (De Boer et al. 1985). Phase values were evaluated only in corresondence with significant coherence values. RESULTS One hundred and twenty-seven of the recorded neurones were selected for analysis. Neurones included in our database dislayed in the PSTHs a tyical resonse to hairy skin recetor stimulation. The resonse was characterized by a eak of facilitation (with latency around 20 ms) followedby a reduction of cell discharge, lasting about 60 120 ms (Mariotti & Formenti, 1990) as shown in Fig. 1A. Noneof the selected neurones showed the resence of sikes in hase with the QRS comlexes of the ECG. Mean frequency of sontaneous tonic firing during wakefulness was 26 9 Hz (s.e.m. ± 1 9). Linear regression between neural discharge and RR interval The time series extracted from the 127 neurones were analysed in terms of their relationshi to RR interval using a linear regression. For 108 cells (85 %) the sloe (a) of the regression line was negative, indicating an imulse activity increasing with the decrease of RR interval. The discharge of 46 neurones (33 %) dislayed a significant correlation (P < 0 01) with the RR interval (Fig. 2). Figure 2. Linear regression between RR interval and neural discharge variability A, examle of time series of RR interval (thin line) and VP sontaneous discharge (thick line) in awake conditions. B, linear regression analysis is erformed on the same eriod. Notice that the sloe of the regression line is negative (a = 5 3) indicating an increased neural firing associated with shorter RR intervals and vice versa. C, smaller arts of the segments on which linear regression has been erformed are suerimosed in order to visually insect the recirocal relationshis between the two variability signals.

J. Physiol. 526.2 VP sontaneous discharge and RR interval 391 Figure 3. Surrogate data test erformed on the variability of neural discharge Examle of surrogate-data analysis. Sectra erformed on the neurogram (300 beats length) extracted from the real signal (thick line) and from the time series derived from ten different surrogate realizations of the same signal (dotted lines). The sectrum of the original signal met the criteria for a coloured rocess. Frequency domain analysis According to the surrogate data test, eochs characterized by non-random fluctuations (below 1 Hz) during wakefulness werefoundin94neuronesoutofthe127(fig. 3). Sectral analysis of RR interval variability revealed the resence of two major oscillatory comonents, the higher one being synchronous with the dominant oscillation detectable in the sectral rofile of the resiratory signal. In Figure 4. Sectral comonents of RR interval, neural discharge variability and resiration Time series of RR interval, VP sontaneous discharge and resiration (Res; left anels) and their variability sectra (right anels). Two oscillatory comonents are detectable in RR and VP sectra, the higher frequency ones being centred around the major resiratory frequency eak. We termed as resiratory-related frequency (RRF) the sectral comonents synchronous with resiration and as nonresiratory-related frequency (NRRF) the lower frequency comonent.

392 M. Massimini, A. Porta, M. Mariotti, A. Malliani and N. Montano J. Physiol. 526.2 our exeriments resiratory rate varied between 0 3 and 0 85 Hz. We termed resiratory-related fluctuation (RRF) the high frequency eak and non-resiratory-related fluctuation (NRRF) the lower frequency one (Fig. 4). Crosssectral analysis between RR interval and neuronal discharge variability revealed a significant coherence (KÂ > 0 5) in at least one of the above frequency bands in 53 out of the 94 (56 %) neurones that assed the surrogate data test. In 37 out of these 53 cases (70 %) coherence was significant in the RRF band (Fig. 5A), and in 16 cases in the NRRF band (30 %) (Fig. 5B). In 12 cases coherence was simultaneouslydetectableinbothbands. Neurograms and tachograms were, in all but three cases, in hase oosition for significant levels of coherence in the RRF band (Fig. 5A). This was the case in only nine out of 16 neurones (56 %) exhibiting coherent fluctuations in the NRRF band (Fig. 5B). Correlation during evoked activity We also analysed eochs of neural activity evoked by airuff stimulation delivered at a frequency of 1 5 Hz. In these cases the low-ass FIR filter was set at 2 Hz in order to include the stimulation rate in the sectral frequency range. The effect of the rhythmic stimulation of the eriheral recetors was clearly visible in the sectral rofile, as a narrow eak centred around 1 5 Hz (Fig. 6). Lower frequencies were also resent, being coherent with the oscillatory comonents detectable in the RR variability sectra in nine out of 17 neurones (Fig. 6). Correlation after slee onset The activity of five neurones dislaying fluctuations coherent with RR interval oscillations during wakefulness was analysed also during slee. In corresondence with a marked EEG synchronization, tonic firing was relaced by burst discharge. A narrow eak after 3 5 ms was observable in the intersike interval histogram. All neurones assed the surrogate data test dislaying a coloured sectral rofile with oscillatory comonents below 1 Hz. During slee, the sectra of both tachogram and neurogram were characterized by a eak in the RRF range. Desite the resence of a very similar sectral rofile on both signals, coherence decreased below the level of significance (K Â < 0 5) in all cases (Fig. 7). Figure 5. Coherence and hase function between RR interval and neural discharge variability A, time series of RR interval (thin line) and VP sontaneous discharge (thick line; left traces). The sectra of both signals are overlotted (uer right anel). A major resiratory related comonent (RRF) can be detected. A significant coherence (K Â) and oosition of hase (dashed line) is detected within this band (lower right anel). B, same as in A, but here sectral ower is concentrated in the range of the nonresiratory-related frequency (NRRF).

J. Physiol. 526.2 VP sontaneous discharge and RR interval 393 Figure 6. Sectral analysis and coherence during evoked activity A, the RR interval variability sectrum (thin line) and the VP discharge variability sectrum (thick line), during evoked activity, are suerimosed. On the discharge variability sectra, a narrow eak, synchronous with the stimulation rate (indicated on the abscissa by a vertical bar) is observable. Slower comonents, in the same frequency range as those resent in RR interval variability, are simultaneously resent. B, cross-sectral analysis, detects the resence of coherent oscillations in the range 0 1 Hz. DISCUSSION The finding of Werner & Mountcastle (1963) has been confirmed by the surrogate data analysis we erformed on our database. Indeed, we found a high roortion of neurones (around 70 %) dislaying coloured sectra in the 0 05 1 Hz frequency range during wakefulness. Oscillations in a similar range are known to be resent in the central and eriheral discharge variability of autonomic neurones and fibres (Langhorst et al. 1986; Malliani et al. 1991; Montano et al. 1992, 1996; Pagani et al. 1997; Lambertz & Langhorst, 1998) and in the variability of RR interval. Numerous studies have already reorted the Figure 7. Sectral analysis and coherence after slee onset Sectral analysis and coherence are calculated over 250 beats eochs before (A) and after (B) slow-wave slee onset. Small segments of RR and VP variability series (20 beats length) are suerimosed and raw (comressed) VP signal is deicted below (left anels). A, tonic discharge and the recirocal relationshi between the two variability signals are visually detectable during wakefulness. B, burst discharge and no recirocal fluctuations are observable during slee: sectral analysis shows similar oscillatory comonents (RRF band) on the two signals during both wakefulness (A, right anels) and slee (B, right anels) while coherence, after slee onset, is lost.

394 M. Massimini, A. Porta, M. Mariotti, A. Malliani and N. Montano J. Physiol. 526.2 resence of two major oscillatory comonents in the short term RR interval and arterial ressure variabilities both in humans and animals (Malliani et al. 1991). The higher frequency oscillation, the so-called resiratory sinus arrhythmia, reresents the neurally mediated effect of resiration on sinus node acemaker activity. This fluctuation is due to the alternating of relative tachycardia and bradycardia associated with the insiratory and exiratory hases of the resiratory cycle, resectively. The low frequency rhythm, on the other hand, is synchronous with arterial ressure vasomotor waves (Mayer waves) and has been attributed to the effect of a symathetic excitatory modulation (Preiss & Polosa, 1974; Malliani, 1999). In our aroach, RR interval variability, an internally generated dynamic, was used as a reference in an effort to interret the sike sequences giving rise to the intersamle variability detectable in VP neurones firing. These sequences are resent in the sontaneous discharge, and they robably fall into the noise band of the discharge distribution histogram when a secific external sensory stimulus is used as a trigger. In our rocedure the neuronal signal was counted, low-ass filtered with a cut-off at 1 Hz and samled in corresondence with each cardiac beat in order to enhance the variability in the exlored frequency range and obtain a synchronous time scale with RR interval series. A first unexected finding was a strong correlation, indeendent of the temoral sequence of the samles revealed by linear regression, between each air of RR interval and neuronal discharge values. In one third of the neurones the level of significance was maintained for eriods of at least 200 beats. Moreover, in 85 % of all analysed neurones the sloe of the regression line was negative (Fig. 2B) indicating that higher levels of neuronal firing were associated with shorter RR intervals and vice- versa. This inverse correlation was not due to transient events since eochs including very slow trends, ossibly related to shifts in the state of vigilance, or ste changes in the series due to movements or sudden arousals, were rejected. Even a simle visual insection of the two suerimosed series reveals the resence of reetitive recirocal variations as exemlified in Fig. 2C. Analysis in the frequency domain indicated that a significant couling between RR interval and neuronal discharge variability referentially occurred in articular frequency bands. Half of the neurones dislaying coloured sectra were coherent with the RR series, and in 70 % of those cells coherence was above 0 5 in the RRF band, corresonding to the resiratory sinus arrhythmia. This was true even when longer eochs (more than 600 beats) were analysed. In almost all cases, oosition of hase between the two variability signals was observed indicating an increase in neural firing in corresondence with the insiratory tachycardia (Fig. 5A). In contrast to our results, Werner & Mountcastle (1963) found no relationshi between the fluctuations in neural discharge rate and resiration, butunder quite different conditions since, in their exeriments, artificial ventilation was erformed on aralysed animals. On the other hand, Chen et al. (1992) detected, in neurones of the medial thalamus, the resence of increased firing rate related to the insiratory hase of integrated hrenic nerve activity for high levels of resiratory drive. This modulation of thalamic neural firing was not necessarily couled to the artificial ventilation-related rhythm being instead the reflection of the central neural resiratory activity. A resiratorymodulated discharge was also observed in neurones located in the midbrain (Chen et al. 1991). We suggest that the RRF observable in our VP neurones consistently reflects a widesread reresentation of the central neural resiratory attern. Since our neurones were identified as resonsive to stimulation of hairy skin eriheral recetors located on the limbs and head it is unlikely that they were affected, through secific recetors and athways, by chest wall movements. Morover, increases in discharge rate related to insiration have been observed in other strucures above the brainstem level such as the amygdala in awake humans and cats (Zhang et al. 1986; Frysinger & Harer, 1988). Couling between neuronal discharge and RR interval variability was not limited to the resiratory band, coherence being above the level of significance also in the lowerartofthesectra(fig. 5B). The NRRF band ranged from 0 07 to 0 3 Hz in our exeriments. Rhythms in this frequency range have been observed by Langhorst in different regions of the brainstem, coherent with cardiovascular variability (Langhorst et al. 1986; Lambertz & Langhorst, 1998). Similar fluctuations in discharge rate, with a eriodicity around 11 s, were reorted by Oakson & Steriade (1982) in the midbrain reticular formation of the cat during both slee and wakefulness. Our animals were not distressed during fixation in the stereotaxic aaratus; cortical EEG was indeed normal and they often fell aslee while in the aaratus. In five cases we could follow the changes in the degree of correlation between neuronal activity and RR interval variability also after slee onset. As exected, a shift from tonic mode to burst discharge was observed in all VP neurones. Desite the ersistence of coloured sectra (< 1 Hz) in neuronal discharge, a significant coherence with the RR variability series was no longer detectable (Fig. 7). In this resect, a slow cortically generated oscillation has recently been characterized both at the EEG and intracellular level in anaesthetized and naturally sleeing cats (Steriade et al. 1993). The slow cortical deolarizing hyerolarizing cycle is transmitted to thalamic reticular and relay neurones and is able to trigger and synchronize the activity of thalamocortical neurones. The slight fluctuations in discharge rate related to cardiovascular variability observable during wakefulness in VP neurones could be disruted, after slee onset, by the owerful effect of a highly synchronized cortically generated oscillation cometing for the same frequency range.

J. Physiol. 526.2 VP sontaneous discharge and RR interval 395 The major finding of our study is that the sontaneous tonic firing of thalamocortical somatosensory neurones is not randomly distributed, having instead an information content. Indeed, during wakefulness, art of the discharge rate fluctuations of these cells is tightly couled to the variability of RR interval, which contains information related to the autonomic regulation of cardiovascular function. Moreover, sectral analysis erformed on eochs of evoked discharge detected the resence of a stimulus-related oscillation, together with slower fluctuations coherent with those resent in RR interval variability. This variability is internally generated and aeared to be relayed by thalamocortical somatosensory neurones to the cortex, together with information conveyed by secific recetors and athways from the external environment. Aart from a few studies in the field of sychohysiology (Nakayama & Hori, 1966), some electrohysiological evidence suggests an effect exherted by vegetative correlates on somatosensory transmission. For examle, resiratory-related modifications have been reorted in cutaneously evoked cortical otentials of man and cats (Shimamura & Mori, 1982), while it has been observed that chemorecetor stimulation is caable of reducing the latency and increasing the robability of discharge of somatosensory thalamocortical neurones in resonse to foreaw stimulation in the anaesthetized rat (Angel & Harris, 1998). In conclusion, these results rovide evidence that RR interval variability is encoded in the sontaneous discharge of thalamic somatosensory neurones. The resence of an imulse activity related to internal basic visceral regulation, within a neural system, characterized by a very high satiotemorally defined coding of external inuts, raises the question whether it may reresent a continuous rhythmic inut integrating andïor modulating the transmission of the somatosensory information to the cortex or whether it is justaninterferencehenomenon. Akaike, H. (1974). A new look at the statistical novel identification. IEEE Transactions on Autonomic Control 19, 716 723. Angel, A. & Harris, M. C. (1998). The effect of chemorecetor stimulation on the centrietal transfer of somatosensory information in the urethane-anaesthetized rat. Neuroscience 86, 321 335. Baselli, G., Porta, A., Rimoldi, O., Pagani, M. & Cerutti, S. (1997). Sectral decomosition in multichannel recordings based on multivariate arametric identification. IEEE Transactions on Biomedical Engineering 44, 1092 1101. Chen, Z., Eldridge, F. L. & Wagner, P. (1991). Resiratoryassociated rhythmic firing of midbrain neurones in cats: relation to thelevelofresiratorydrive. Journal of Physiology 437, 305 325. Chen, Z., Eldridge, F. L. & Wagner, P. (1992). Resiratoryassociated thalamic activity is related to level of resiratory drive. Resiration Physiology 90, 99 113. De Boer, J. M., Karemaker, M. & Strackee, J. (1985). Relationshis between short-term blood ressure fluctuations and heart rate variability in resting subjects. Medical and Biological Engineering and Comuting 23, 352 358. Frysinger, R. & Harer, R. M. (1989). Cardiac and resiratory correlations with unit discharge in human amygdala and hiocamus. Electroencehalograhy and Clinical Neurohysiology 72, 463 470. Hirsch, J. C., Fourment, A. & Marc, M. E. (1983) Slee-related variations of membrane otential in the lateral geniculate body relayneuronesofthecat. Brain Research 259, 308 312. Jahnsen, H. & Llin as, R. (1984). Electrohysiological roerties of guinea-ig thalamic neurones: an in vitro study. Journal of Physiology 349, 205 206. Kay, S. M. & Marle, S. L. (1981). Sectrum analysis: a modern ersective. Proceedings of IEEE 69, 1380 1418. Lambertz, M. & Langhorst, P. (1998). Simultaneous changes of rhythmic organization in brainstem neurons, resiration, cardiovascular system and EEG between 0 05 Hz and 0 5 Hz. Journal of the Autonomic Nervous System 68, 58 77. Langhorst, P., Lambertz, M. & Shulz, G. (1986). Assessment of rhythmicity in the visceral nervous system. In Neural Mechanisms and Cardiovascular Diseases, ed. Lown, B., Malliani, A. & Prosdocimi, M.,. 133 144. Liviana Press, Padova. Langhorst, P., Schulz, G., Seller, H. & Koechen, H. P. (1996). Convergence of visceral and somatic afferents on single neurones in the reticular formation of the lower brainstem. Journal of the Autonomic Nervous System 57, 149 157. McCormick, D. A. & Bal, T. (1997). Slee and arousal: thalamocortical mechanisms. Annual Review of Neurosciences 20, 185 215. Malliani, A. (1999). The attern of symathovagal balance exlored in the frequency domain. News in Physiological Sciences 14, 111 117. Malliani, A., Pagani, M., Lombardi, F. & Cerutti, S. (1991). Cardiovascular neural regulation exlored in the frequency domain. Circulation 84, 482 492. Mariotti, M. & Formenti, A. (1990). Somatosensory transmission through the ventroosterolateral thalamic nucleus during wake and slee. The Dyencehalon and Slee, ed. Mancia, M. & Marini, G.,. 301 311. Raven Press Ltd, New York. Montano, N., Gnecchi-Ruscone, T., Porta, A., Lombardi, F., Malliani, A. & Barman, S. M. (1996). Presence of vasomotor and resiratory rhythms in the discharge of single medullary neurons involved in the regulation of the cardiovascular system. Journal of the Autonomic Nervous System 57, 116 122. Montano, N., Lombardi, F., Gnecchi-Ruscone, T., Contini, M., Finocchiaro, M. L., Baselli, G., Porta, A., Cerutti, S. & Malliani, M. (1992). Sectral analysis of symathetic discharge, RR interval, and systolic arterial ressure in decerebrate cats. Journal of the Autonomic Nervous System 40, 21 32. Morf, A., Viera, T. L. & Kailath. T. (1985). Recursive multichannel maximum entroy sectral estimates in the study of electrical brain activity. Biological Cybernetics 51, 239 247. Oakson, G. & Steriade, M. (1982). Slow rhythmic rate fluctuation of cat midbrain reticular neurons in syncronized slee and waking. Brain Research 247, 277 288. Nakayama, T. & Hori, T. (1966). Cortical evoked otential and subjective sensation to electric stimulation of the skin: effects of osture and resiratory movement. Jaanese Journal of Physiology 16, 612 624. Pagani, M., Montano, N., Porta, A., Malliani, A., Abboud, F. M., Birkett, C. L. & Somers, V. K. (1997). Relationshi between sectral comonents of cardiovascular variabilities and direct measures of muscle symathetic nerve activity in humans. Circulation 95, 1441 1448.

396 M. Massimini, A. Porta, M. Mariotti, A. Malliani and N. Montano J. Physiol. 526.2 Preiss, G. & Polosa, C. (1974) Patterns of symathetic neuron activity associated with Mayer waves. American Journal of Physiology 226, 724 730. Schulz, B., Lamberts, M., Schulz, G. & Langhorst, P. (1983). Reticular formation of the lower brainstem. A common system for cardioresiratory and somatomotor functions: discharge atterns of neighboring neurons influenced by somatosensory afferents. Journal the of Autonomic Nervous System 9, 433 449. Schulz, B., Lamberts, M., Schulz, G. & Langhorst, P. (1985). Reticular formation of the lower brainstem. A common system for cardioresiratory and somatomotor functions. Cross-correlation analysis of discharge atterns of neighboring neurons. Journal of the Autonomic Nervous System 12, 35 62. Shimamura M. & Mori, A. (1982). Sensory evoked otentials and their changes with resiration in mnd cat. Electroencehalograhy and Clinical Neurohysiology, sul. 36, 297 304. Steriade, M., Contreras, D., Curro Dossi, D., Nuñez, A. (1993). The slow (< 1 Hz) oscillation in reticular thalamic and thalamocortical neurons: scenario of slee rhythm generation in interacting thalamic and neocortical networks. Journal of Neuroscience 13, 3284 3299. Steriade, M. & Deschḙnes, M. (1984). The thalamus as a neuronal oscillator. Brain Research Reviews 8, 1 63. Theiler, A., Eubank, S., Longtin, A. & Galdrikian, J. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica 58, 239 247. Werner, G. & Mountcastle, V. B. (1963). The variability of central neural activity in a sensory system, and its imlications for the central reflection of sensory events. Journal of Neurohysiology 26, 958 977. Zhang, J., Harer, R. M. & Frysinger, C. (1986). Resiratory modulation of neuronal discharge in the central nucleus of the amygdala during slee and waking state. Exerimental Neurology 91, 193 207. Acknowledgements We gratefully acknowledge Renato Calcaterra for his helful technical assistance and Marzia Martina and Mario Rosanova for their highly valued collaborative work. Corresonding author M. Massimini: Diartimento di Scienze Precliniche Laboratorio Interdiscilinare Tecnologie Avanzate di Vialba, Universita degli Studi di Milano, Italy, Osedale Luigi Sacco, via Giovan Battista Grassi 74, 20157 Milano, Italy. Email: marcello@mailserver.unimi.it