B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
ava i l ab l e a t www.sc i enced i rec t . com
www.e l sev i e r. com/ l oca te /b ra in res
Research Report
Interaction of slow cortical rhythm with somatosensoryinformation processing in urethane-anesthetized rats
Attila Totha,⁎, Erika Gyengesia, Laszlo Zaborszkyb, Laszlo Detaria
aDepartment of Physiology and Neurobiology, Eötvös Loránd University, Budapest, HungarybCenter for Molecular and Behavioral Neuroscience, Rutgers the State University of New Jersey, Newark, NJ, USA
Article history:Accepted 27 May 2008Available online 5 June 2008
Slow cortical rhythm (SCR) is a rhythmic alteration of active (hypopolarized), and silent(hyperpolarized) epochs in cortical cells. SCR was found to influence sensory informationprocessing in various models, but these studies yielded inconsistent results. We examinedsensory processing in anesthetized rats during SCR by recording multiple unit activity(MUA) and evoked field potentials (eFPs). Evoked field potentials as well as spontaneous FPchanges around spontaneous activations were analyzed by subsequent current sourcedensity (CSD) analysis. MUA responses and eFPs were recorded from the hindlimb area (HL)of the somatosensory cortex (SI) to electrical stimuli of the tibial nerve during active andsilent states, respectively. Stimulus-associated MUA above the ongoing background activitydid not differ significantly in active vs. silent states. Short-latency (<50 ms) eFP responsesconsisted of a sequence of deep-negative and deep-positive waves. Parameters of the firstnegative deflection were similar in both states. Stimulation in the silent state occasionallyinduced 500–700 ms long spindles in the alpha range (10–16 Hz). Spindles were neverobserved in responses to active state stimulation. CSD analysis showed moderatelydifferent cortical sink–source patterns when the stimulus was applied during active vs.silent state. Sinks first appeared in layer IV, V and VI, corresponding sources were in layer I/II, V and VI. Stronger activation appeared in the infraganular layers in the case of activestate. CSD of spontaneous FPs revealed some sequential activation pattern in the cortexwhen strongest and earlier sink appeared in layer III during active states.
Keywords:Current source density analysisEvoked field potentialHindlimb areaRatSomatosensory cortexSlow cortical rhythm
1. Introduction
In both anesthesia and natural sleep, a low-frequency (<1 Hz)rhythmic activity, slow cortical rhythm (SCR) is present in thecortical electroencephalogram(Steriade etal, 1993a; Steriade et al,1993b). A similar pattern has been reported in cortical slabs(Timofeev et al, 2000), and in neocortical slices (Sanchez-Vives
and McCormick, 2000). Slow cortical rhythm can have differentmanifestations depending on the type and depth of anesthesiaand on the depth of natural sleep, but in deep urethane anes-thesia it is characterized by the alternation of almost isoelectricalEEG periods with short epochs of activity consisting of waves atdifferent frequencies (Grahn and Heller, 1989; Détári et al, 1997).Recording with transcortical electrodes revealed that the active
ram; eFP, evoked field potential; FP, field potential; HL, hindlimb;atosensory cortex; TC, thalamocortical
Fig. 1 – Multi-unit responses to tibial nerve stimulation during different phases of the SCR in the hindlimb area ofsomatosensory cortex. Top: original multiple unit and EEG recordings from a representative rat. Bottom: grand averageperi-stimulus time histograms (PSTHs) of multiple unit responses during active and silent states, respectively, from fiveindividual rats. Bin width for PSTHs was 1 ms.
100 B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
epochs start with a sudden, deep-negative potential shift (GrahnandHeller, 1989; Détári et al, 1997). This shift is caused by a strongdepolarization in pyramidal aswell as in non-pyramidal neurons(Metherate and Ashe, 1993). Hyperpolarization and virtually nosynaptic activity are seen in pyramidal cells during the isoelec-trical period (silent state), while strong synaptic activity andaction potentials ride the depolarizing wave in the active state(Metherate and Ashe, 1993). Active (depolarized) states were firstreferred as UP states, while silent (hyperpolarized) epochs asDOWN states in the basal ganglia (Wilson and Kawaguchi, 1996).Strong correlationwas found between SCR states andmembranepotential fluctuations of striatal medium spiny neurons (Kasa-netz et al, 2006), hippocampal interneurons (Hahn et al, 2006) andcortical neurons (Steriade et al, 1993a; Haider et al, 2006;Hasenstaub et al, 2007; Haider et al, 2007). SCR was declared tobe of cortical origin as thalamic ablations did not abolish corticalactive and silent states (Steriade et al, 1993b) and cortical abla-tions prevented active states from occurring in the striatum andthalamus (Nita et al, 2003; Steriade et al, 1993b). The SCR issynchronized over the corticalmantle (Volgushev et al, 2006), andalso reaches subcortical targets, including the striatum (Wilsonand Kawaguchi, 1996), basal forebrain (Szentgyorgyi et al, 2006),subthalamic nucleus–globus pallidus (Magill et al, 2000), hippo-
Interaction of active and silent stateswith cortical informa-tion processing has been examined in several studies. N20component of the somatosensory evokedpotentialswas foundto be larger when stimuli were applied during the depolarizedstate compared to responsesevokedduring thehyperpolarizedstate in human experiments (Massimini et al, 2003). Somato-sensory stimulation evoked responses with larger amplitudewhen stimuli were applied during the depolarized state in cats(Rosanova and Timofeev, 2005). UP and DOWN states werefound to modulate sensory-evoked postsynaptic potentialsand action potentials in the rat barrel cortex. In theseexperiments, stimuli were more effective in the DOWN state(Sachdev et al, 2004; Petersen et al, 2003). In a recent study,however, more complex interactions were found between SCRand evoked responses in the barrel cortex (Hasenstaub et al,2007). UP and DOWN states alter processing of not only soma-tosensory, but visual stimuli as well in cats (Arieli et al, 1996;Azouz and Gray, 1999; Anderson et al, 2000; Haider et al, 2007).However, relatively few data are available from rats fromcortical areas other than the barrel cortex. Therefore, we
Fig. 2 – Averaged eFPs (n=50; left) from the HL area of the SI and their corresponding CSD profiles (right) in case of active andsilent state stimulation, respectively. Data presented here are derived from a representative rat. In the middle, cortical layersaremarkedwith numberswhile lines indicate the borders of the layers. Stimulus arrived at 0ms. Sinks aremarked by numbers(1–3) while sources marked by letters (a–c).
101B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
focused on the hindlimb area (HL) of the primary somatosen-sory cortex (SI), which is less investigated compared to thebarrel/vibrissae system. The aim of the present experimentswas to examine the influence of SCR states to informationprocessing in urethane-anaesthetized rats by applying elec-trical stimuli to the tibial nerve and recording neuronal andfield responses in the HL area of the SI. In addition to theevoked field potentials (FPs), we also analyzed spontaneous FPdata recorded from this area during transitions between activeand silent states.
2. Results
2.1. Unit activity
Spontaneous MUA in the HL area of the SI showed strongcorrelationwith the SCR.High frequency spike activitywas seenduring active states, while cells virtually ceased firing during
silent states (Fig. 1, top row). Thisdifference is clearly seen in the50ms longbaseline part of PSTHs (Fig. 1, bottomrow) created forstimuli applied during active and silent states, respectively.Number of spikes during the 50 ms pre-stimulus period wassignificantly (p<0.05; Student's paired t-test) higher in the activestate (1.987±0.786 spikes) than in the silent state (0.573±0.418spikes). Somatosensory stimuli induced activation of unit ac-tivity in both states of the SCR (Fig. 1, bottom row). Unit res-ponses lasted for about12ms inbothactiveandsilent statesandshowed two characteristic peaks at about 9 and 20 ms. Theamplitudeof the firstpeakwas largerby59%duringactive statescompared to silent states. Similarly, the second peak was alsolarger by 16% in active states. However, due to the differentbaseline levels, only 1.22±0.87 spikes were seen above back-ground firing in active states while 1.58±1.06 spikes wereevoked in silent states. The difference was not significant(Student's paired t-test). After termination of the response,MUAreturned to the baseline level in the case of active state stimuli,while a long, moderate activation was seen in silent states.
Fig. 3 – Occurrence of spindles in response to stimulation during the silent state. Spindles were never observed in responses tostimuli given in active state. Panel A, left: representative single responses recorded in active and silent states, respectively.Panel A, right: power spectra calculated for epochsmarked by vertical broken lines. Panel B shows responses and power spectrafrom another rat. In this rat, curveswere high pass filtered at 3 Hz. In case of silent state responses, peaks belonging to the alpharange (14.65 Hz and 11.72 Hz, respectively) are seen in the power spectra in both rats. These peaks are missing in the powerspectra of the active state responses.
Fig. 4 – Spontaneous FP activity in the HL area of the SI. Data presented here are derived from a representative rat. Left:averaged spontaneous FPs (n=50 epochs). Middle: CSD profile of the spontaneous activity. Cortical layers are marked withnumbers while lines indicate the borders of the layers. Sinks are marked by Roman numbers (I.–VI.) while sources marked bycapital letters (A–E). On the left and middle panels, vertical dotted lines indicate the approximate borders of an active statebetween 350 and 700 ms. Between 700 and 1000 ms, a part of a silent state is seen. Right: relative amplitudes of the EEG shiftsseen during active states. The graph also shows the polarity of the shifts. Positive shifts appeared in the supragranular layersand in the granular layer while negative shifts were present in the infragranular ones. For comparison, shift amplitude on thecortical surface was given as a reference (with a value of 1).
102 B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
Fig. 5 – CSD profile of spindles evoked by stimuli applied in silent state of the SCR in one rat. Spindles are represented by twoseries of current sinks in layer II/III and in layer V with an onset latency about 100 ms. Corresponding sources are located inlayer IV. All three series of sinks and sources were closely associated with each other. Left: averaged eFPs (n=50). Right: CSDprofile. In themiddle, cortical layers aremarkedwith numberswhile lines indicate the borders of the layers. Stimulus arrived at0 ms. Averaged eFPs were notch filtered between 48 and 52 Hz.
103B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
2.2. Evoked FPs
Tibial nerve stimuli elicited clear evoked potentials duringboth phases of SCR. Short-latency responses (up to 50 ms)consisted of a sequence of negative–positive waves (activestate: 1–4, silent state: 1–2; see a representative example onFig. 2, left). The onset latency of the first deep-negative wavewas virtually the same in active and silent states (9.83±1.48 ms and 9.88±1.46 ms, respectively). Similarly, no differ-ence was seen in the peak latency (15.78±4.04 ms and 15.78±4.02 ms, respectively). Peak amplitude was slightly larger insilent state compared to active state in all individual animals(n=6) and in the grand average (315.03±118.50 μV and 278.58±112.95 μV, respectively), but the differencewas not statisticallysignificant (Student's paired t-test). In all but one case (5 out of6 rats), a second depth-negative wave appeared in active stateresponses (latency: 25.82±2.79 ms). In contrast, this secondwave appeared only in half of the animals in response to silentstate stimulation (3 out of 6 rats) with a latency of 25.80±2.01 ms.
Longer latency components of the response showed astronger dependency on the SCR. In silent states, short-latencyresponses were occasionally followed by spindles in the alpharange (10–16 Hz). Spindles started at 100–120 ms after thestimulus and lasted for 500–700ms. In all, but one animal, thesepatterns were eliminated by the averaging procedure (Fig. 3),thus their phase was not stimulus-locked. Spindles were neverobserved in responses to stimuli given in the active state.
2.3. Spontaneous FPs
Spontaneous FPs recorded from the HL area of the SI showedlow-frequency (<1 Hz) oscillation between large EEG shiftswith superposed high-frequency activity (active states) andintermittent silent states (Fig. 4, left). The shifts showedcharacteristic polarity as well as amplitude distribution.Positive shifts appeared in the supragranular layers and in
the granular layer while negative shifts were recorded in theinfraganular ones (Fig. 4, left). To facilitate comparison of shiftamplitudes, ratios of maximal values (measured between thedotted lines) to the value recorded on the cortical surface arealso indicated (Fig. 4, right). The largest shift was present inlayer VI at a depth of 1500 μmwith an amplitude almost twicecompared to the surface value. The smallest amplitude shiftappeared in the middle of the cortex at about 1050 μmwith anamplitude about one fifth (0.206) compared to the reference.
2.4. CSD analysis
2.4.1. Spontaneous activityCortical CSD profiles showed a complex series of sink–sourcepatterns during spontaneous SCR epochs. For the sake ofclarity, sinks weremarked by Roman numbers (active state: I.–III.; silent state: IV.–VI.) while sources by capital letters (activestate: A–C; silent state: D–E). During active states, a character-istic sink appeared in layer III (sink I.; see Fig. 4, middlecolumn, between 400–600ms). Sink I clearly showed two fusedparts which reflected the two large waves that appeared in thespontaneous FPs during active states in the upper layers(Fig. 4, left). Compared to sink I, which was strong and had along duration (>200ms), a smaller, shorter and slightly longer-latency sink was present in the deeper part of the layer V,centered at around 1200 mm (sink II). A third sink alsoappeared (sink III) at the border of layer VI and the whitematter. It had the longest latency among the three active statesinks. Corresponding sources appeared in layer I/II (source A)and in layer VI (source B and source C). Source B appeared inthe upper part of the layer (at ≈1500 μm). Source C wasgenerated in the deeper part (at ≈1800 μm) and it was shorterand weaker compared to source B. Among the sources thatappeared during active states, source C showed the shortestlatency. When the activate state ended and the silent onebegan (see Fig. 4, middle, from about 700 ms), the previoussink–source configuration reversed. In the locations where
Fig. 6 – Location of the recording sites in the hindlimb area of somatosensory cortex. Frontal sections are derived from thestereotaxic atlas of Paxinos and Watson (1998).
104 B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
sinks were found during active state, sources showed up insilent state and vice versa (for example, see the source A →sink IV or the sink I→ source D transitions). This phenomenon
resulted in a “chessboard-like” pattern. Sink III represented anexception to this rule, as this was not followed by a sourceduring silent state.
105B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
2.4.2. Evoked responsesSimilar to the spontaneous activity, multiple series of sink–source patterns were found after somatosensory stimulationin both states of the SCR (Fig. 2, right). In this case, sinks weremarked by Arabic numbers (1–3) while sources by letters (a–c).Data are expressed as mean±SD (n=4 in most cases). Mo-derate differences were only seen in the sink–source patternsfollowing stimulation in active vs. silent state of SCR.
Hindlimb stimulation applied during active as well asduring silent states evoked a strong sink in layer IV (sink 1;Fig. 2, right) with two peaks separated by 10 ms. The first peakwas more pronounced in the silent state than the second one,while they were similar in strength in the active state. Onsetlatency of sink 1 was very similar in both cases (10.18±1.48 msin active and 10.33±1.58 ms in silent state). This sink was alsopresent in layer III in the active state, while it was transfer-red to layer III with a delay of about 15 ms in the silent state.Sink 1 persisted for 153.40 ms±67.01 ms in active state and for168.75±99.45 ms in silent state, and was associated with twostrong sources. Source B in the infragranular layer (layer V–VI)clearly corresponded to the first part of sink 1 in layer IVshowing two distinct peaks. It had a significantly longerduration in the active state then in the silent state (123.85±5.62 ms and 24.35±6.14 ms, respectively; p<0.05; Student'spaired t-test). Source A in the supragranular layer (layer I–II)was locked more to the part of sink 1 in layer III. It appearedsimultaneously with source B in the active state, but wasdelayed in the silent state clearly following the spread of sink 1to layer III. An additional source (source C) appeared in layer VIbetween the two peaks of source B with a similar latency inboth sates (17.73±5.98 ms in active, 20.48±5.23 ms in silent,respectively). It wasmore pronounced in the silent than in theactive state.
A short-latency sink (sink 2) also appeared in layer VI. Itstiming and intensity reflected the characteristics of sink 1 inlayer IV. Two peaks could be distinguished in the active statein parallel with the peaks of sink 1, while only one, weak peakwas seen in the silent state during the first stronger peak ofsink 1 in this state (see Fig. 2).
In the active state, another sink was observed in some ratsthat depended even more on the ongoing cortical activity. Itappeared in layer V in 3 out of the 4 rats in case of active statestimuli, but it was present only in one rat after silent statestimulation. Sink 3 was delayed compared to sink 1 and sink 2with an onset latency of 31.7±9.85ms (n=3) in the active state.In the single casewhen it was present after silent state stimuli,it had an onset latency of 23.3 ms (data not shown).
Sink–source configuration during spindles in the alpharange evoked by stimuli applied in the silent state was alsoexamined. Spindleswere associatedwith two series of sinks inlayer II/III and in layer V, respectively (Fig. 5). Correspondingsources appeared in layer IV. All three series of sinks andsources were in close temporal association with each other.
2.5. Histology
Histology confirmed that recording sites were located in fivecases in the lateral, in one case in themedial part of theHL areaof the SI between −1.30 and −1.40 mm from Bregma (Fig. 6). Incontrol recordings from adjacent cortical regions outside the
coordinates of the HL area, no responses (MUA or eFPs) wereobserved (data not shown). Laminar analysis showed that inboth active and silent states the largest amplitude responseswere recorded from layer V (4/6 animals) or at the border oflayer V and VI (2/6 animals) at an average depth of 916 μm±285 μm from the cortical surface. The average thickness of thecortex at the HL area was 1874±184 μm.
3. Discussion
Weexamined the influence of SCRon somatosensory informa-tion processing by first recording multiple unit activity thenevoked and spontaneous FPs with subsequent CSD analysis.Our results show that tibial nerve stimulation in urethane-anaesthetized rats is able to evoke responses during bothstates of the SCR. Stimulus-associatedMUA above the ongoingbackground activity in SI did not differ significantly in activevs. silent states (Fig. 1). Amplitude of the short-latency eFPstended to be larger during silent states, while peak latencieswere the same during both states (see Fig. 2, left). CSD analysisof the eFPs indicated stronger activation in the infragranularlayers following stimuli applied during active cortical statescompared to silent ones (Fig. 2, right).
In contrast to the original concept of Steriade (Steriade et al,1993c; Timofeev et al, 1996), recent data have shown thatsensory information can reach the cortex in both phases of theSCR. However, it is still not clear, how SCR influences (thalamo)cortical information processing. Experiments addressing thecortical effects of various sensory inputs during different(thalamo)cortical states yielded contradicting results in differ-ent species aswell as in different studies using the same animalmodel. Neuronal responses evoked by whisker stimulation inthe rat barrel cortex were larger during DOWN states comparedto UP states (Petersen et al, 2003; Sachdev et al, 2004). However,in a recent study, these results were only verified for thepresentationof short and simple stimuli.Whenaprolongedandvariable (more natural) whisker stimulation pattern was used,increased responsiveness was found during UP states (Hasen-staubetal, 2007).Central (in this case, thalamic) stimulationwasalsomore effective at evoking spikes in theDOWNstate, than inthe UP state in rats (Sachdev et al, 2004). We used single, shortsomatosensory stimuli and found similar responses after activeaswell as silent state stimulation. Responses tended to be largerto silent state stimulation but the difference between active vs.silent state responseswasnot significant.Thus, our resultswerein agreement with the results of previous studies in the barrel/vibrissae system, though we did not test the effect of the longand variable stimuli. In contrast, visual stimuli evoked morespikes and larger depolarization in the cat visual cortex, whenpresented in the depolarized state (Arieli et al, 1996; Azouz andGray, 1999; Anderson et al, 2000; Haider et al, 2007). In ferretslices in vitro, more action potentials were evoked during the UPstate then in the DOWN state in response to the stimulation ofthe white matter (Shu et al, 2003). The contradictory data fromcatsandrats raises thepossibility of speciesdifferencesas itwassuggested recently (Haider et al, 2007).
Thus, evoked responses can be larger during silent or DOWNstates compared to the active or UP ones in case of single stimuli.Several factors can contribute to this phenomenon. Some factors
106 B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
can play a facilitating role in this issue (i.e. can increase theamplitude of the response), but others can play a preventing role.Whenwestudy thisquestion,wemaysee theoverall summaryofall these pro and con factors. Membrane resistance differencesduring UP and DOWN states can be pro factors. In UP states,neurons have a higher background activity, but their membraneresistance is about one fifth of that measured during DOWNstates, at least inanaesthetizedcats (DestexheandPare, 1999) andrats (Cowan and Wilson, 1994). Because of the higher resistance,synaptic currents can induce largermembrane potential changesduring DOWN states then in UP states leading to increasedamplitude of eFPs. Spike threshold seems to be alsohigher duringUP states (Hasenstaub et al, 2007). Activity changes of inhibitoryneurons may also be important in influencing the amplitude ofthe eFP. Peripheral stimulation was found to activate bothexcitatory and inhibitory neurons in layers II–V of the cortex(Armstrong-James et al, 1992; Armstrong-James and Fox, 1987).Increased baseline activity levels of cortical neurons mean thatinhibitory circuits are also more active during UP states then inDOWN states (Hasenstaub et al, 2005). Thus, external stimuliarriving during active states may induce more activation ininhibitory neurons compared to silent states. In other words,balance between excitation and inhibition in the cortex may beshifted toward excitation in response to a stimulus arrivingduring silent state leading to increased responsiveness duringthis cortical state. Thalamic mechanisms may also play animportant role, as during DOWN states, a possible increase inbursting of thalamic relayneuronswas suggested (Timofeev et al,1996; Rosanova and Timofeev, 2005).
Our CSD data are generally in agreement with previousstudies applying central or peripheral stimulation anddescribing CSD profiles in the SI (Castro-Alamancos andConnors, 1996; Kandel and Buzsaki, 1997; Jellema et al, 2004).However, there are two basic differences between our experi-ments and the other reports. Firstly, we analyzed separatelythe effects of stimuli given in different phases of slow corticalrhythms. Secondly, CSD analysis in previous studies wasperformed only for the first 30 ms (Castro-Alamancos andConnors, 1996; Jellema et al, 2004) or 50 ms of the post-stimulation period (Kandel and Buzsaki, 1997). We found sinksand sources bearing longer durations than 30 or 50 ms thusextended the analysis to longer periods after stimuli. Thus,our CSD data give information for the first time about short-and midlatency sink and source patterns generated by corti-cal circuits in the HL area of the SI in response to peripheralstimuli applied during different phases of the SCR in anes-thetized rats.
Specific TC afferents terminate mainly in layer IV andlower part of the layer III in the SI (Jensen and Killackey, 1987;Herkenham, 1980). A less dense projection formed by collat-erals of incoming thalamic fibers also terminates in layer VI(Zhang and Deschenes, 1998). The thalamic afferents establishsynapses on dendrites of about all types of neurons at theirdepth of termination, but mainly on pyramidal and stellate(granule) cells. Stellate cells have spherical dendrite arboriza-tion, thus generate closed-field potential changes and con-tribute minimally to CSD. However, axons of these cells areimportant in the spread of activity within the cortex as theiraxons together with axon collaterals of pyramidal neuronsascend and descend to form synapses on the apical dendrites
of layer II, III and V pyramidal cells (Armstrong-James et al,1992).
In agreement with the anatomical description of thiscircuitry, the earliest sinks (sink 1 and 2) in our experimentsappeared in layers IV and VI reflecting the activity arrivingthrough primary thalamic afferents (Fig. 2). Shortly after itsinitialization, sink 1 shifted toward layer III, as it was alreadydescribed in earlier studies (Castro-Alamancos and Connors,1996; Kandel and Buzsaki, 1997). In these publications, theupward shift was suggested to be induced directly by thalamicafferents terminating on the basal dendrites of layer IIIpyramidal cells and indirectly by axon collaterals of granulecells converging on the same neurons. In our experiments, asink in layer III together with a corresponding source in layer Iwas already present before the stimulus arrived in the activestate (Fig. 2). Currents induced by the stimulus were summedwith these preexisting currents leading to the early appear-ance of the upper component of sink 1 in layer III together withits source in layer I. Neither the generation of the active state,nor its spread to the whole cortical mantle and to subcorticalstructures is well understood. However, it seems from ourdata, that pyramidal cells in layer III have an important role inthese processes. Our CSD data of the spontaneous activitysupport this hypothesis as a strong sink appeared in layer IIIduring active states (sink I, see Fig. 4, middle), which precededthe sink in layer V (sink II).
In an earlier study, the median nerve was stimulatedelectrically and EPswere recorded in the forelimb area of the SIin ketamine anaesthetized rats (Jellema et al, 2004). Theauthors found a short-latency sink–source configuration inlayer Vb, and suggested that it reflected the population spikeof layer Vb pyramidal cells. In addition, the authors describeda superficial sink in layer I–II. These sinkswere not observed inour experiments and were not detected by other authors(Castro-Alamancos and Connors, 1996; Kandel and Buzsaki,1997). We found a characteristic sink in layer V (sink c), but itwas much more delayed and appeared mostly after activestate stimulation (see Fig. 2). These differences in sink–sourcepatterns might be attributable to different technical details(anesthesia, spatial resolution of the CSD analyses, recordingsites in the SI).
Infraganular layers (layer V and VI) appeared to be moreactive during active state compared to the silent one (Fig. 2) incase of somatosensory stimulation. Sink 2 was larger both inamplitude and duration as well as source b in case of activestate stimuli. Sink 3 was absent in silent state with only oneexception. These differences may reflect the different corticalinformation processing mechanisms. It can be hypothesizedthat activity of thalamic afferents terminating in layer VI maybe lower in silent state compared to the active one. Absence ofsink 3 in silent state (Fig. 2) may show the presence of a cellpopulation in layer V which is activated through local circuitswhen the stimulus arrives in active state, but not in the silentstate. In general, active states may allow for all sort ofpolysynaptic mechanisms that could account for late eventsas seen in case of source b and sink 3 (Fig. 2, active).
We also examined the cortical current generators of spon-taneous FPs in the HL area of the SI with CSD analysis. Asshown on Fig. 4, polysynaptic activation during active statesresulted in a strong sink in layer III (sink I.) and a weaker and
107B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
longer-latency sink in layer V (sink II.). No sink was found inlayer IV and only a weak sink appeared at the ventral border oflayer VI (sink III.). This is in agreement with the idea thatthalamic input is not involved in the generation of activestates (Steriade et al, 1993b; Nita et al, 2003) as the abovementioned layers receive most of the thalamic input (Jensenand Killackey, 1987; Herkenham, 1980; Zhang and Deschenes,1998). SCR was suggested to originate from layer V based on invitro data (Sanchez-Vives and McCormick, 2000) and to be – atleast, partly – a travelling wave (Sanchez-Vives and McCor-mick, 2000; Massimini et al, 2004; Luczak et al, 2007). Our CSDdata may reveal some sequential activation in cortical circuitsas activation in layer III (sink I.) appeared earlier compared tolayer V (sink II.) during active states (Fig. 4, middle).
Long-latency evoked potential components are moredifficult to interpret than short-latency ones because of thepossible recruitment of various neuronal circuits in time.Characteristic long-latency components of evoked responsesin our experiments were spindles in the alpha range (10–16 Hz). Spindles were only observed when stimuli were givenin silent state (Fig. 3). It was suggested previously (Steriadeet al, 1993b) that sensory stimuli can only induce spindles insilent states due to the deinactivation of the low threshold Ca-spike mechanism in thalamic reticular and relay neurons(Destexhe et al, 1999). Any excitatory input reaching the cellsin this state would result in fast Na-spike bursts riding theslow Ca-spike (Steriade et al, 1993c; Timofeev et al, 1996).These bursts in turnwould induce a short spindle sequence byinteracting with the reticular nucleus. In contrast, our experi-ments showed that short-latency responses to peripheralstimuli were similar in active and silent states, though theywere often followed by short spindle sequences in silentstates. Our CSD data indicated that evoked spindles wererepresented by a series of sinks in layers II/III and V, whilecorresponding sources were located in layer IV (Fig. 5). Thefrequency of these spindle waves fell in the alpha (10–16 Hz)range. With TC spindles, sinks in layer IV would be expected,induced by the incoming spike volleys of thalamic relayneurons. It was previously suggested that, in contrast tospindles, alpha waves are generated in the cortex itself(Steriade et al, 1990). As sinks during the evoked spindles inour experiments were found in layer V (and not in layer IV), itmight be possible that these waves were generated by thecortex in response to the stimuli. In our experiments, spindlesin the alpha range appeared in layer IV and mostly in layer V.However, spontaneous spindles were found to peak in thegranular layer (layer IV), to appear with smaller amplitude inthe supragranular layers, and missing in the infragranularlayers in unanesthetized animals (Kandel and Buzsaki, 1997).
In summary, our results give some insight into theneuronal processes underlying sensory information proces-sing during the different states of the SCR. While these statesreflect strong differences at the neuronal level, surprisinglysmall differences were found between responses to stimuligiven during the active and silent state, respectively. Futurestudies using naturally sleeping animals may provide a betterunderstanding of the generation and spread of slow corticalrhythm and might help to elucidate the functional signifi-cance of this characteristic rhythm in the sensory informationprocessing.
4. Experimental procedures
4.1. Surgical and recording procedures
Male Sprague–Dawley rats (n=6), weighing between 320 and 410 gwere anesthetized with urethane (1.2 g/kg, i.p.) and fixed in astereotaxic frame (David Kopf) with the top of the skull sethorizontal to complywith the atlas of Paxinos andWatson (1998).A 4×3mmcraniotomy centered 1.5mmposterior to Bregma and1.5 mm lateral to the midline was made to expose the HLrepresentation area in the right hemisphere. The duramater wasleft intact during the whole experiment. Rectal temperature wasmaintainedat 37 °Cwithaheatingpadattached to a thermostaticinstrument. Supplementary doses of urethane were given ifneeded. Experiments were carried out in accordance with theEuropean Communities Council Directive of 24 November 1986(86/609/EEC) andwith the guidelines of the local Animal Care andUse Committee.
4.2. Electrophysiological recordings
MUA was recorded from the SI with extracellular metal micro-electrodes (tip resistance2MW;A-M-Systems, Sequim,WA,USA).A stainless steel screw placed above the cerebellum served asreference. The microelectrode was slowly moved by a hydraulicmicrodrive (Narishige,Tokyo, Japan) toadepthof 1–1.2mmbelowthe dura. The signal was filtered, amplified (300 Hz–5 kHz, gain:10000×; A-M Systems) and displayed by an oscilloscope. EEG wassimultaneouslymonitored fromthecontralateralHLareaof theSI(A: Br −1.3 mm, L 2.2 mm) with a bipolar transcortical electrodeand conditioned with the same amplifier (filter: 0.1 Hz–500 Hz,gain: 1000×).
The exposed part of the SI was systematically explored tolocate the area where neurons responded to light mechanicalstimulation of the left hindpaw. When responsive units werefound, two stimulating needle electrodes were inserted into theHL close to the ankle to enable electrical stimulation of the tibialnerve, and unit responses were recorded to electrical stimulationof the nerve. The threshold stimulus voltage was defined as thelowest intensity that elicitedminimalmovement in the hindpaw(6.92 V±3.67 V). During the whole experiment, stimulus strengthtwice the thresholdwas used. Duration of the stimuliwas 90 μs inall cases.
Transitions to hypopolarized, active states were clearlyindicated by an increase in MUA, and by a sharp, deep-negativeshift with superposed high-frequency activity in the EEGrecorded from the contralateral hemisphere. These changesappear almost simultaneously, though there may be a minimaldelay on FP level compared to the level of units (Kasanetz et al,2006). Selective stimulation during active and silent states wasachieved by feeding the EEG signal into a comparator. Whendeep negativity reached a pre-set level during the transition tothe active state, the comparator triggered a stimulator (Master8,AMPI, Jerusalem, Israel). Selection of a short stimulus delay(50 ms) ensured that the stimulus arrived during the fullydeveloped active state. A delay of 600 ms from the onset of thedeep-negative EEG shift (see Fig. 1, right) resulted in stimuli thatarrived in the silent state, when cortical neurons had recoveredfrom the previous depolarization caused by the active state.A total of 50–50 stimuli were applied during active and silent
108 B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
cortical states, respectively. Unit responses were recorded in2000 ms long blocks (1000 ms pre-stimulus, 1000 ms post-stimulus periods).
When this part of the recording session was completed, themetal microelectrode was pulled back from the brain. A smalldrop of Fast Green dye (Sigma) was placed on the tip of themicroelectrode and the electrode was reinserted into thesuperficial layers of the cortex to mark the exact location ofthe HL area. Then the microelectrode was replaced by a 16-channel vertical electrode array (Elektroencefalograf Ltd., Buda-pest, Hungary) to record spontaneous and evoked FPs fromdifferent cortical layers at the same recording site. Recordingcontacts of the array were made of 40-μm platinum–iridiumwires, with an interelectrode distance of 150 μm. Impedance ofthe recording sites was taken as 1 MΩ at 100 Hz (Ulbert et al,2001). At the tip of the array, an additional 50-μmstainless steelwirewas built into thearray to enablemarkingof recording sitesby depositing iron ions for a subsequent Prussian blue reaction.
Following insertion of the electrode array, tibial nerve stimuliwere applied during active and silent states (50–50 stimuli,respectively) and eFPs were recorded (length 2000 ms; 1000 mspre-stimulus – 1000 ms post-stimulus). EEG signals were fed to a16-channel differential amplifier (Supertech Ltd., Pecs, Hungary),conditioned (filter: 0.1 Hz–1000Hz, gain: 5000×) then digitalized at3 kHz with 16 bit resolution (Labview; National Instruments,Austin,TX,USA). All datawerestoredondisk foroff-lineanalysis.
4.3. Data analysis
Analysis was carried out off-line using custom designedsoftware. The program enabled visual inspection of therecorded signals, digital filtering, spectral analysis of the EEGsignal, averaging of single evoked responses and calculation offiring rates from selected epochs of neuronal activity.
ToanalyzeMUA, spikeswere separated fromnoise by settinga threshold level. Artifacts were eliminated by spike shapeinformation (Detari et al, 1997). Peri-stimulus time histograms(PSTHs) were calculated to examine the changes of neuronalactivity before and after stimulation (bin width: 1 ms).
Evoked FPs were recorded separately in active and silentstates, and averaged using a custom-writtenMatlab (MathWorks,Inc., Natick,MA, USA) script. In the first step, stimulus timingwasvisually examined and erroneously timed stimuli (e.g. whenstimulus arrived in active state instead of silent state and viceversa)were excluded fromaveraging. EFPs recorded during activestates were riding on the deep-negative EEG shifts characterizingactive states. To facilitate analysis of the evoked potentials,negative shifts recorded without stimulation were averaged andsubtracted fromtheaveragedcurves recordedduringstimulation.From the 16 signals recorded by the electrode array, the channelon which the first negative wave appeared with the shortestlatency and largest amplitude was selected for eFP analysis.Amplitude and latency differences seen in active vs. silent stateresponses as well as parameters of individual sinks and sourceswere compared statistically by paired, two-tailed Student's t-tests. Statistical testswereperformedusing Instat (GraphPad, SanDiego, CA, USA). Statistical significance was accepted at thep<0.05 level.
One-dimensional current source density (CSD) analysiswas performedusing the averaged spontaneous and evoked FP
data of the 16-pole recording array to accurately locate thesynaptic currents inducing the local extracellular potentialchanges. CSD plots were calculated by approximating thesecond spatial derivative of voltage using a custom-writtenMatlab script. CSD calculation used the following formula(Mitzdorf, 1985):
where CSD(h,t) is the CSD at a fixed time t and depth h, Φ(h,t) isthe averaged eFP at time t and depth h. In our experiments, Δhwas 150 μm, while three points Hamming spatial filter wasapplied (n=3). To eliminate background noise, threshold levelswere defined using maximum values in a given data set. Inmost cases, 20% of the maximum value was used.
4.4. Histology
At the end of the experiment, a 50 μA positive direct currentwas passed through the stainless steel electrode at the tipof the electrode array for 60 s. The animals were perfusedtranscardially with 150 ml of 0.9% saline followed by 400 ml offixative containing 4% of paraformaldehyde and 2% ofpotassium ferrocyanide to produce a Prussian blue reactionwith the iron ions deposited from the electrode. Brains wereremoved and postfixed overnight at 4 °C in the same fixative.Coronal sections (50 μm) were cut with a vibroslicer, mountedand stained in gallocyanine solution overnight. After dehydra-tion, sliceswere coverslippedwithCanada balsam. Bright-fieldlight-microscopy was used to locate recording sites, whichwere marked on the appropriate plates of the stereotaxic atlasof Paxinos and Watson (1998).
The thickness of the cortex at the recording site and thedistance of the iron deposit from the cortical surface weremeasured on photographs taken by an Olympus BFX51 micro-scope equipped with an Fview-2 CCD camera. Photographs wereanalyzed using an image-analysis program (Analysis; Olympus,Japan). Distances were measured in parallel with the apicaldendrites of pyramidal cells. Borders of cortical layers weredetermined by inspecting the microphotographs and comparingthemwith data available in the literature (Skoglund et al, 1996). Inthis way, position of recording points of the electrode array indifferent cortical layers was determined. Sub-layers Va and Vbwere not distinguished within layer V.
Acknowledgments
This research was supported by the National Institute ofHealth grant NS-23945 to L. Zaborszky and by OTKA grant (K68445) to L. Detari.
R E F E R E N C E S
Anderson, J., Lampl, I., Reichova, I., Carandini, M., Ferster, D., 2000.Stimulus dependence of two-state fluctuations of membranepotential in cat visual cortex. Nat. Neurosci. 3, 617–621.
Arieli, A., Sterkin, A., Grinvald, A., Aertsen, A., 1996. Dynamics ofongoing activity: explanation of the large variability in evokedcortical responses. Science 273, 1868–1871.
109B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
Armstrong-James, M., Fox, K., 1987. Spatiotemporal convergenceand divergence in the rat S1 bbarrel" cortex. J. Comp Neurol.263, 265–281.
Armstrong-James, M., Fox, K., Das-Gupta, A., 1992. Flow ofexcitation within rat barrel cortex on striking a single vibrissa.J. Neurophysiol. 68, 1345–1358.
Azouz, R., Gray, C.M., 1999. Cellular mechanisms contributing toresponse variability of cortical neurons in vivo. J. Neurosci. 19,2209–2223.
Balatoni, B., Detari, L., 2003. EEG related neuronal activity in thepedunculopontine tegmental nucleus of urethaneanaesthetized rats. Brain Res. 959, 304–311.
Castro-Alamancos, M.A., Connors, B.W., 1996. Spatiotemporalproperties of short-term plasticity sensorimotorthalamocortical pathways of the rat. J. Neurosci. 16,2767–2779.
Cowan, R.L., Wilson, C.J., 1994. Spontaneous firing patterns andaxonal projections of single corticostriatal neurons in the ratmedial agranular cortex. J. Neurophysiol. 71, 17–32.
Destexhe, A., Contreras, D., Steriade, M., 1999. Cortically-inducedcoherence of a thalamic-generated oscillation. Neuroscience92, 427–443.
Destexhe, A., Pare, D., 1999. Impact of network activity on theintegrative properties of neocortical pyramidal neurons invivo. J. Neurophysiol. 81, 1531–1547.
Détári, L., Rasmusson, D.D., Semba, K., 1997. Phasic relationshipbetween the activity of basal forebrain neurons and corticalEEG. Brain Res. 759, 112–121.
Detari, L., Semba, K., Rasmusson, D.D., 1997. Responses of corticalEEG-related basal forebrain neurons to brainstem and sensorystimulation in urethane-anaesthetized rats. Eur. J. Neurosci. 9,1153–1161.
Grahn, D.A., Heller, H.C., 1989. Activity of most rostralventromedial medulla neurons reflect EEG/EMG patternchanges. Am. J. Physiol. 257, R1496–R1505.
Hahn, T.T., Sakmann, B., Mehta, M.R., 2006. Phase-locking ofhippocampal interneurons' membrane potential to neocorticalup-down states. Nat. Neurosci. 9, 1359–1361.
Haider, B., Duque, A., Hasenstaub, A.R., McCormick, D.A., 2006.Neocortical network activity in vivo is generated through adynamic balance of excitation and inhibition. J. Neurosci. 26,4535–4545.
Haider, B., Duque, A., Hasenstaub, A.R., Yu, Y., McCormick, D.A.,2007. Enhancement of visual responsiveness by spontaneouslocal network activity in vivo. J. Neurophysiol. 97, 4186–4202.
Hasenstaub, A., Shu, Y., Haider, B., Kraushaar, U., Duque, A.,McCormick, D.A., 2005. Inhibitory postsynaptic potentials carrysynchronized frequency information in active corticalnetworks. Neuron 47, 423–435.
Herkenham, M., 1980. Laminar organization of thalamicprojections to the rat neocortex. Science 207, 532–535.
Jellema, T., Brunia, C.H., Wadman, W.J., 2004. Sequentialactivation of microcircuits underlying somatosensory-evokedpotentials in rat neocortex. Neuroscience 129, 283–295.
Jensen, K.F., Killackey, H.P., 1987. Terminal arbors of axonsprojecting to the somatosensory cortex of the adult rat. I. Thenormal morphology of specific thalamocortical afferentsJ. Neurosci. 7, 3529–3543.
Kandel, A., Buzsaki, G., 1997. Cellular-synaptic generation of sleepspindles, spike-and-wave discharges, and evokedthalamocortical responses in the neocortex of the rat.J. Neurosci. 17, 6783–6797.
Kasanetz, F., Riquelme, L.A., O'Donnell, P., Murer, M.G., 2006.Turning off cortical ensembles stops striatal Up states andelicits phase perturbations in cortical and striatal slowoscillations in rat in vivo. J. Physiol 577, 97–113.
Luczak, A., Bartho, P., Marguet, S.L., Buzsaki, G., Harris, K.D., 2007.Sequential structure of neocortical spontaneous activity invivo. Proc. Natl. Acad. Sci. U.S.A 104, 347–352.
Magill, P.J., Bolam, J.P., Bevan, M.D., 2000. Relationship of activity inthe subthalamic nucleus-globus pallidus network to corticalelectroencephalogram. J. Neurosci. 20, 820–833.
Massimini, M., Huber, R., Ferrarelli, F., Hill, S., Tononi, G., 2004. Thesleep slow oscillation as a traveling wave. J. Neurosci. 24,6862–6870.
Massimini, M., Rosanova, M., Mariotti, M., 2003. EEG slow(approximately 1 Hz) waves are associated withnonstationarity of thalamo-cortical sensory processing in thesleeping human. J. Neurophysiol. 89, 1205–1213.
Metherate, R., Ashe, J.H., 1993. Ionic flux contributions to neocorticalslowwaves and nucleus basalis-mediated activation: whole-cellrecordings in vivo. J. Neurosci. 13, 5312–5323.
Mitzdorf, U., 1985. Current source-density method and applicationin cat cerebral cortex: investigation of evoked potentials andEEG phenomena. Physiol Rev. 65, 37–100.
Nita, D.A., Steriade, M., Amzica, F., 2003. Hyperpolarisationrectification in cat lateral geniculate neurons modulated byintact corticothalamic projections. J. Physiol 552, 325–332.
Paxinos, G., Watson, C., 1998. The Rat Brain in StereotaxicCoordinates, 4th ed. Academic Press, London.
Petersen, C.C., Hahn, T.T., Mehta, M., Grinvald, A., Sakmann, B.,2003. Interaction of sensory responses with spontaneousdepolarization in layer 2/3 barrel cortex. Proc. Natl.Acad. Sci. U.S.A 100, 13638–13643.
Rosanova, M., Timofeev, I., 2005. Neuronal mechanismsmediatingthe variability of somatosensory evoked potentials duringsleep oscillations in cats. J. Physiol 562, 569–582.
Sachdev, R.N., Ebner, F.F., Wilson, C.J., 2004. Effect of subthresholdup and down states on the whisker-evoked response insomatosensory cortex. J. Neurophysiol. 92, 3511–3521.
Sanchez-Vives, M.V., McCormick, D.A., 2000. Cellular and networkmechanisms of rhythmic recurrent activity in neocortex. Nat.Neurosci. 3, 1027–1034.
Shu, Y., Hasenstaub, A., McCormick, D.A., 2003. Turning on and offrecurrent balanced cortical activity. Nature 423, 288–293.
Skoglund, T.S., Pascher, R., Berthold, C.H., 1996. Aspects of thequantitative analysis of neurons in the cerebral cortexJ. Neurosci. Methods 70, 201–210.
Steriade, M., Amzica, F., Contreras, D., 1994. Cortical and thalamiccellular correlates of electroencephalographicburst-suppression. Electroencephalogr. Clin. Neurophysiol.90, 1–16.
Steriade, M., Contreras, D., Curro, D.R., Nunez, A., 1993a. Theslow (<1 Hz) oscillation in reticular thalamic andthalamocortical neurons: scenario of sleep rhythm generationin interacting thalamic and neocortical networks. J. Neurosci.13, 3284–3299.
Steriade, M., Gloor, P., Llinas, R.R., Lopes de Silva, F.H., Mesulam,M.M.,1990. Report of IFCN Committee on basic mechanisms. Basicmechanisms of cerebral rhythmic activitiesElectroencephalogr. Clin. Neurophysiol. 76, 481–508.
Steriade, M., Nunez, A., Amzica, F., 1993b. A novel slow (<1 Hz)oscillation of neocortical neurons in vivo: depolarizingand hyperpolarizing components. J. Neurosci. 13, 3252–3265.
Steriade, M., Nunez, A., Amzica, F., 1993c. Intracellular analysis ofrelations between the slow (<1 Hz) neocortical oscillation andother sleep rhythms of the electroencephalogram. J. Neurosci.13, 3266–3283.
Timofeev, I., Contreras, D., Steriade, M., 1996. Synapticresponsiveness of cortical and thalamic neurones duringvarious phases of slow sleep oscillation in cat. J. Physiol494 (Pt 1), 265–278.
110 B R A I N R E S E A R C H 1 2 2 6 ( 2 0 0 8 ) 9 9 – 1 1 0
Timofeev, I., Grenier, F., Bazhenov, M., Sejnowski, T.J., Steriade, M.,2000. Origin of slow cortical oscillations in deafferented corticalslabs. Cereb. Cortex 10, 1185–1199.
Ulbert, I., Halgren, E., Heit, G., Karmos, G., 2001. Multiplemicroelectrode-recording system for human intracorticalapplications. J. Neurosci. Methods 106, 69–79.
Volgushev, M., Chauvette, S., Mukovski, M., Timofeev, I., 2006.Precise long-range synchronization of activity and silence inneocortical neurons during slow-wave oscillations [corrected].J. Neurosci. 26, 5665–5672.
Wilson, C.J., Kawaguchi, Y., 1996. The origins of two-statespontaneous membrane potential fluctuations of neostriatalspiny neurons. J. Neurosci. 16, 2397–2410.
Wolansky, T., Clement, E.A., Peters, S.R., Palczak,M.A., Dickson, C.T.,2006. Hippocampal slow oscillation: a novel EEG state and itscoordination with ongoing neocortical activity. J. Neurosci. 26,6213–6229.
Zhang, Z.W., Deschenes, M., 1998. Projections to layer VI of theposteromedial barrel field in the rat: a reappraisal of the role ofcorticothalamic pathways. Cereb. Cortex 8, 428–436.
