Notably in gene expression experiments, the expression of desat1 closely tracked Clk, indicating that desat1 may be regulated directly by an output mechanism of the cell-autonomous oenocyte clock, possibly via the transcriptional regulators of the Clk gene, VRILLE and PDP1ε ( Allada and Chung, 2010), or possibly by CLK itself. Consistent with the possibility of direct regulation, consensus binding sites or VRI, PDF1ε, and CLK are present within the desat1 locus ( Figure S2A). Genetic manipulations affecting PDF expression also affected the display of cuticular hydrocarbon compounds, including the male sex pheromones 7-T, 5-T, and 7-P. Loss of Pdf or Pdfr expression reduced sex pheromone expression,

while misexpression

Bortezomib nmr of Pdf increased these compounds. We suggest that these effects on pheromone expression reflect asynchrony between components of the circadian system, those being primarily the central pacemaker neurons and the oenocyte clock. In the absence of phase information provided by the CNS via PDF, the SCH727965 oenocyte clock and by extension the circadian expression of desat1 may become uncoupled from rhythms in other physiological and behavioral processes necessary for proper pheromonal output. In this way, seemingly subtle changes in phase may lead to a misalignment between rhythms and an amplified response in physiological output. Several studies have demonstrated daily rhythmicity in courtship and mating (Hardeland, 1972, Sakai and Ishida, 2001 and Tauber et al., 2003), thus implicating the circadian system in the regulation

of sexual behavior in Drosophila. Recently, others have shown that the PDF-expressing vLNs are involved in mediating a male sex drive C1GALT1 rhythm (MSDR), a novel activity rhythm displayed by males when individually paired with a female and allowed to interact continuously for 24 hr ( Fujii and Amrein, 2010 and Fujii et al., 2007). Our results extend these findings by demonstrating that the circadian system not only influences courtship and mating but also regulates the physiology mediating the production and display of chemical signals critically important to sexual behavior. We propose that the PDF signaling pathway and its ability to synchronize the activity of peripheral and central oscillators may couple reproductive physiology with behavior. In this regard, we suggest that the PDF signaling pathway may act at two levels: within the individual (i.e., the male fly), PDF signaling may influence both sexual characteristics (pheromone expression) and sex drive, while between individuals of the group, PDF-dependent effects on male pheromone expression may alter female mating behavior. Studies in several organisms have demonstrated that fitness benefits of the circadian system are evident in a light/dark cycle but not in constant conditions or when out of phase with environment cues (Dodd et al.

The synaptic response to pairs of stimuli (PPR, see Supplemental Experimental Procedures) can be used as an indirect measure of Pr. We found that PPR is not significantly different between genotypes (Figure 3C). TSA HDAC Taken together, our results demonstrate that reduction in quantal size contributes to only a fraction of the reduced synaptic strength in −/y mice. Because Pr is not altered, we conclude that there must also be a significant decrease in the number of release sites that each RGC makes on a given relay neuron of −/y mice. This mechanism is similar to that described at autaptic hippocampal synapses (Chao et al., 2007), although other studies with densely

cultured hippocampal neurons or hippocampal slices from Mecp2 mutant mice find a disruption in the Pr ( Asaka et al., 2006 and Nelson et al., 2006). Mechanisms underlying synaptic weakening may vary depending on culture conditions and the specific synapse studied. Our physiological selleck chemicals llc data show that the retinogeniculate circuit becomes abnormal in −/y mice after P21. We asked whether these changes are a result of failure to maintain refined axon projections, a process that has been described in mice with disrupted retinal activity ( Demas et al., 2006). Retinal axons organize into eye-specific regions in the LGN in a process that is thought

to be largely complete by P8–P10 in mice ( Godement et al., 1984 and Jaubert-Miazza et al., 2005). To address whether eye-specific segregation is disrupted in the mutant, we injected both eyes with two different β cholera toxin-conjugated

fluorescent dyes to visualize the terminal fields of ipsi- and contralateral retinal projections to the LGN. We quantified segregation by using an unbiased assay that analyzes, for each pixel, Glucocerebrosidase the logarithm of the ratio of fluorescence intensity from each fluorescence channel (R value) ( Torborg and Feller, 2004). The variance of R, defined as the width of the histogram distribution of R values, can be used to compare segregation patterns. High variance indicates a high degree of segregation, whereas low variance indicates a high degree of overlap (see Supplemental Experimental Procedures). By using this analysis, we did not observe a significant difference in the segregation pattern of retinogeniculate projections between −/y and +/y mice at P27–P34. However, by P46–P51, a modest but significant difference in segregation was noted (Figure 4). These results are consistent with our physiological findings that the initial formation and refinement of this synaptic circuit are relatively normal in mutant mice and functional defects arise only during a later, experience-dependent period of development. At the mouse retinogeniculate synapse a vision-dependent sensitive period for synaptic remodeling begins around the age of P20.

The percentage of unimodal cells decreased with the distance from the border of the respective

primary area (solid lines in Figure 5C, left). Conversely, bimodal cells were uniformly distributed, with a slight increase in the middle of the field of view. To test for a gradient in the density of unimodal Venetoclax nmr cells along the V1-S1 axis, we performed a linear regression on the cell-density values (dashed lines in Figure 5C, left) For unimodal cells, the slopes were significantly different from zero (see Figure 5C, right; slopes: −0.066 for T cells, p = 0.017; 0.078 for V cells, p = 0.005; permutation test for the slope; see Supplemental Experimental Procedures). The distribution of bimodal cells did not show a spatial gradient

(−0.012 for M-driven cells, p = 0.68; permutation test for the slope). Moreover, we failed to find a similar gradient for a modality dominance index—which expresses the relative strengths of the two modalities—computed on responses of bimodal cells (Figure S4B). This indicated that bimodal cells near one primary cortex were not functionally dominated by the corresponding modality. We then wondered whether the three types of responsive neurons showed some kind of spatial clustering on a microscale level. Since the gradient of unimodal neurons could be find more a confounding factor, we restricted our analysis to the middle stripe of the imaged area (i.e., a portion of RL oriented orthogonal to the V1-S1 axis and equidistant from both S1 and V1—see also additional controls in Table S2). Within this stripe the mean position and density of cell

somata along the V1-S1 axis were statistically indistinguishable for V, T, and M cells, indicating a homogenous distribution of unimodal neurons within the central cortical stripe (Figures S4C and S4D). We performed a nearest-neighbor analysis in the center of area RL for V, T, and M cells separately on single optical planes (Komiyama et al., 2010; Figure 5D). As the three cell types had a similar density, we took 0.33 as a chance probability for the nearest Phosphatidylethanolamine N-methyltransferase neighbor analysis (Figure 5E). For each cell type, we first computed the probability of having a nearest neighbor of a certain type. For unimodal cells, the probability that the nearest neighbor was another unimodal neuron of the same modality was above chance (Figure 5E; for T cells: 52.5% of T neighbors, p < 0.001; for V cells: 55.7% of V neighbors, p < 0.001) and the probability that the nearest neighbor was a unimodal neuron but driven by the other modality was below chance (for T cells: 17.1% of V neighbors, p < 0.01; for V cells: 20.0% of T neighbors, p < 0.05). For unimodal cells, the probability that the nearest neighbor was bimodal did not differ from chance. Conversely, for bimodal cells, the nearest neighbor could either be a T, V, or M cell, with a trend toward M cells (29.4% of T neighbors, p = 0.58, 30.9% of V cells, p = 0.27, 39.

At the level of immunolabeling, expression of stem cell markers BMN 673 in vivo was abolished in iN cells,

consistent with a conversion of H1 ESCs into iN cells (Figure 2A). Quantitative RT-PCR analyses revealed that iN cells expressed increased levels of endogenous Ngn2 as well as of two neuronal markers, NeuN and MAP2, whose levels were elevated ∼100-fold (Figure 2B). In addition, we observed an even larger induction of the expression of the transcription factors Brn2 and FoxG1, which are markers for excitatory cortical neurons (Figure 2B). Immunoblotting experiments showed that the neuronal precursor cell (NPC) markers nestin and Sox2 were only detectable in the ESCs and iPSCs, whereas a series of well-established synaptic genes were only expressed in 3-week-old Ngn2 iN cells (Figures selleck compound 2C and S2A). Quantitative RT-PCR measurements of the expression of the NPC markers Sox2 and nestin in the first 2 weeks after Ngn2 induction revealed a transient brief increase in these markers immediately after induction, with a rapid decline in expression (Figure S2B). Furthermore, upon coculture with mouse astrocytes, H1-cell-derived iN cells formed synapses with each other and with cocultured COS cells expressing neuroligin-1 (Figures S2C–S2E). Thus, iN cell generation involves a switch from a stem cell to a neuronal gene expression phenotype with stimulation of endogenous

Ngn2 expression. Measurements of the yield of iN cell conversion in three stem cell lines, H1 ESCs and two different iPSC

lines, showed that nearly 100% of surviving lentivirally infected ESCs and iPSCs were converted into neurons, revealing an unprecedented efficiency of conversion (Figure 2D). When we calculated the number of iN cells generated as a function of starting ESCs or iPSCs, we observed an apparent increase with H1 ESC-derived iN cells but not with the two iPSC-line-derived iN cells (Figure 2D). The increase in cell numbers Cediranib (AZD2171) in H1 ESC-derived iN cells is due to the continuing division of H1 cells after plating; iPS-cell derived iN cells do not show such increased cell numbers because they exhibit some cell death in response to culture splitting and lentiviral infection, resulting in a partial loss of the iPSCs as iN cells are being generated. Overall, these data demonstrate that forced expression of a single transcription factor—Ngn2—induces neuronal differentiation with high yield. We next aimed to gain insight into the nature of the neurons generated and, more importantly, to assess the reproducibility of Ngn2-induced production of iN cells from different ESC and iPSC lines. Toward this end, we quantitatively analyzed expression of 73 genes at the single-cell level using fluidigm-dependent mRNA measurements (Pang et al., 2011; Table S1). All fluidigm-mediated quantitative RT-PCR assays were validated using standard curves (Table S1).

For each imaging field, neural responses were imaged to ten www.selleckchem.com/products/BKM-120.html whisker stimulations spaced 10 s apart. The analyses of changes in fluorescence were restricted to a 2 s window immediately following the onset of whisker stimulation. A total of 816 cells were imaged in seven fear-conditioned mice, and 833 cells in six explicitly unpaired control mice. Cortical networks are spontaneously active, and this spontaneous activity must be considered when defining evoked responses. To examine spontaneous activity we measured

changes in fluorescence in a 2 s time window immediately following each of ten sham whisker stimulations delivered with the same temporal pattern as during actual trials (Figure 3B and Movie S2). We used the resulting statistics of spontaneous activity for two purposes: (1) to examine if associative fear learning affected PF 2341066 spontaneous activity, and (2) to define thresholds of response magnitude (Figure 3C) and fidelity (Figure 3D) above which a neuron was considered responsive in subsequent trials with an actual stimulus. Here, mean response magnitude refers to the average fluorescent change across all ten sham stimuli, and fidelity refers to the number of sham trials out of ten that were temporally coincident with a given neuron’s spontaneous activity (see Experimental Procedures). Importantly, there were no significant differences in spontaneous

activity between paired and PI-1840 explicitly unpaired groups, as measured by mean response magnitude (Figure 3C: paired 1.17% ± 0.06%; unpaired 1.16% ± 0.03% dF/F, p = 0.14), mean response fidelity (Figure 3D paired 1.61; unpaired 1.66, p = 0.48) and network synchrony (Ch’ng and Reid, 2010 and Golshani et al., 2009) (Figure 3E, two-way ANOVA training effect F[1,320] = 1.4, p = 0.24). The values of spontaneous response magnitude (Figure 3C), and fidelity (Figure 3D) derived from sham stimuli were then used to determine the threshold for defining with 95% confidence whether a neuron was actually responding to

whisker stimulation or simply happened to be spontaneously active at the moment of whisker stimulation. For magnitude of response (dF/F), the 95% cutoff in paired mice was a 3.2% increase in fluorescence above baseline, and for explicitly unpaired mice was 2.7% above baseline (see gray shading in Figure 3C). For fidelity, the 95% cutoff was 4; that is, only 5% of cells were spontaneously active during the sham stimulus more than four out of ten trials (gray shading in Figure 3D). Using these thresholds, neurons could be confidently defined as responsive based on their mean response magnitude or based on the fidelity of their response. To determine whether associative learning impacts network coding of the CS we imaged cortical responses evoked by stimulation of the trained whisker (Figure 4 and Movie S3).

, 1993). Although INaP remains to be measured in cortical mammalian nodes, noninactivating channel openings underlying INaP have been directly recorded in the frog node BMS-387032 price of Ranvier ( Dubois and Bergman, 1975). What makes the node prone to generating persistent Na+ current? Although the precise molecular basis of persistent Na+ current is still elusive, the steady current flow most likely reflects a gating mode of the conventional Na+ channel. In the model of Taddese and Bean (2002), persistent and transient Na+ currents could be explained by a single Na+ channel,

assuming that the inactivating particle binds weakly to Na+ channels in the resting inactivated state but strongly to already activated channels. This scheme predicts larger INaP

when more Na+ channels are in the resting condition, when inactivation Cobimetinib in vivo is incomplete and weak and membrane densities are high. Large axonal INaP might be a simple consequence of the high density and the specific gating properties of the axonal Na+ channel isoform. First, compared to the soma, the voltage dependence of activation and inactivation of axonal Na+ current is shifted 10 mV to more hyperpolarized potentials, and consequently, a larger fraction of Na+ channels are in the resting inactivated state ( Kole et al., 2008 and Schmidt-Hieber and Bischofberger, 2010). Second, Nav1.6, which is the main isoform in adult myelinated axons ( Boiko et al., 2001 and Lorincz and Nusser, 2010), is in particular prone to entering a noninactivating state ( Rush et al., 2005). Finally, recent estimations suggest that nodes of Ranvier express Nav1.6 at even higher densities compared to the AIS ( Lorincz learn more and

Nusser, 2010). How does a subthreshold current in the node influence excitability in the AIS? The AIS and first node are separated by a single ∼50–100 μm long internodal and myelinated section, suggesting a tight electrotonic coupling. This idea is supported by whole-cell recordings showing that steady-state voltage attenuation in the first ∼150 μm of the axon is small (<10%, Kole et al., 2007). During ongoing synaptic activity, the first node may be activated with voltage fluctuations nearly similar to the AIS, placing the first node in a strategic position to integrate synaptic inputs and modulate output generated in the AIS. A steady inward current near the site of AP initiation could effectively reduce the electrical load of the first internodal section, leading to a more hyperpolarized axosomatic AP voltage threshold. Based on the somatically recorded INaP, axons cut before the branchpoint had an ∼250 pA reduced persistent current at −50 mV ( Figure 6). Given an average input resistance of 20 MΩ, the reduced INaP may thus fully account for the ∼5 mV (ΔV = ΔI × R) more depolarized AP voltage threshold observed during steady depolarizations.

We applied CsF-DIDS in repatches of seven cells after having collected a sufficient number of ripple-associated

cPSCs under control conditions close to the potential of Cl− reversal. In line with our hypothesis, ripple-associated fast synaptic inputs indeed persisted in the repatch recording with disrupted GABAAR-mediated learn more synaptic transmission (Figure 6C). We again analyzed downward and upward slopes of putative EPSCs and compared their values before and following perfusion of the cells with CsF-DIDS. Moreover, ripple-locked downward cPSC slopes were unchanged following intracellular block of inhibition (control: 24.3 ± 0.8 pA/ms, n = 224 cPSCs; CsF-DIDS: 26.6 ± 0.7 pA/ms, n = 462 cPSCs; 7 repatched cells; p = 0.1; K-S test), whereas upward slopes were slightly enhanced (control: 12.9 ± 0.3 pA/ms; CsF-DIDS: 13.9 ± 0.2 pA/ms; Figure 6D; p < 0.0001; K-S test). Additionally, we examined the intervals between

successive downward slopes. Distributions peaked at 4–5 ms, consistent with ripple frequency, both in control conditions and after CsF-DIDS administration (Figure 6E; see Figure S6B for single-cell data). Taken together, these results derived from experimentally blocking the somatic postsynaptic action of GABAergic inputs corroborate our hypothesis that ripples are accompanied by a strong oscillation-coherent phasic excitatory component. We next asked whether Buparlisib in vivo Diclofenamide ripple-coherent cPSCs represent the spiking output of CA3 pyramidal neurons (Both et al., 2008)

or whether they are generated locally within the CA1 network. We used “minislices” where area CA1 was isolated from the adjacent CA3 and subiculum (Figures 7A and 7C). In this experimental system, we observed SWRs at a rate of 0.46 ± 0.09 Hz (median: 0.46 Hz; range: 0.13 Hz to 0.93 Hz; 8 CA1 minislices; Figure 7B). Ripple frequency in these events was 213.1 ± 6.6 Hz on average (median: 215 Hz; range: 175 Hz to 235 Hz; Figure 7B, right). To test whether ripple-coherent cPSCs survived in the isolated area CA1, we again recorded from principal neurons voltage-clamped close to the reversal potential of Cl− (−66 mV). SWRs in CA1 minislices were indeed accompanied by phasic inward currents at ripple frequency that were also phase coherent with LFP ripples (Figures 7D–7E; n = 725 cPSCs; 5 cells). Moreover, in minislices, cPSC downward slope phases with respect to LFP ripples (−101° ± 8°, Figure 7F) were comparable with those derived from intact slices (−114° ± 10°, Figure 4E). In summary, this set of experiments demonstrates the possibility of a local origin of ripple-coherent excitatory PSCs within area CA1. The observation that excitatory PSCs are phasic and ripple-locked raised the question of whether they could account for the timing of action potentials in target CA1 principal neurons.

Finally, we used one more parameter that we refer to as risk bonus (as distinct from optimal risk bonus scaling), which was used in neural and behavioral analyses. This was the difference in value modification in favor of the riskier choice compared to the safer choice. It was calculated using the optimal risk bonus scaling as: equation(6) Riskbonus=optionbonusriskier−optionbonussafer.Therefore, risk bonus reflects the relative change in value of the riskier choice, compared

to the safer choice, which occurs as a function of risk pressure and the magnitude and probability characteristics of both choices in a given trial. We note that, in this regard, our model is an optimal model that serves to BAY 73-4506 clinical trial motivate definitions of terms but that real subjects Palbociclib order may not be completely optimal. For example, if, instead, option bonuses were only adjusted as a function of their reward magnitudes (rather than as a function of both reward magnitudes and probabilities; Equation 3) then the resulting risk bonus regressor would be correlated at r = 0.96 with the

regressor that we used. In summary, the approach allows us to (1) examine decision making in the context of the varying impact of risk pressure and (2) conceive of the impact of risk pressure as a quantifiable modifying influence on a default decision-making process. However, we explore an alternative approach in the Supplemental Experimental Procedures that considers how an agent with sufficient experience of a set of contexts may use a reinforcement learning model to estimate the values of choices. A number of links between the approaches are identified and discussed. This work RNASEH2A was funded by the Wellcome Trust and the Medical Research Council. We thank Jacqueline Scholl, Bolton Chau, and Rei Akaishi for their very helpful suggestions

and advice on the manuscript. “
“(Neuron 80, 1277–1289; December 4, 2013) We would like to correct a label in Figure S2 in the Supplemental Information of our recent publication. In panel B of this figure, the rate map under the label “trajectory 2” incorrectly corresponded to “trajectory 3” and vice-versa, as it can be ascertained by their shape. We have now corrected the panels’ positions in the Supplemental Information online. We present our apologies and thank the reader who pointed this out for us. “
“(Neuron 81, 484–503; February 5, 2014) The original publication contained errors in gene nomenclature in Table 2. The table has now been corrected in the article online. “
“For information to flow through the nervous system, neurons must become subdivided into distinct axonal and dendritic domains. Given the importance of this process, neuronal polarity establishment has been a topic of intense study for many years. However, although many possible signaling pathways have been identified, relatively little is known about how a developing neuron interprets these cues to establish polarity.

, 2012) and toxoplasmosis in sheep and humans (Hide et al., 2009). Despite the efforts of previous studies to confirm this transmission route in horses (Duarte et al., 2004 and Locatelli-Dittrich et al., 2006), many points are still unclear, including the relationship between the level of antibodies in mares and the frequency of vertical transmission of

these agents in the Sarcocystidae family. Therefore, the aim of study was to correlation levels of antibodies in mares with pre colostral foals seropositive and assess the level and distribution of antibodies against Neospora spp., S. neurona and T. gondii, in mares and pre colostral foals an the parturition We obtained 181 samples from mares, without clinical history of neurological and reproductive ON-01910 datasheet diseases, and their newborns, in Rio

Grande do Sul, Brazil. The blood was drawn by jugular puncture from mares during parturition and from their newborns before colostrum intake. The whole blood was centrifuged at 250 × g for 10 min to separate serum, which was stored at −20 °C until tested. This research was licensed by the Ethics and Animal Experimentation Federal University of Santa Maria, with number 81/2009. Neospora caninum (NC-1 strain) and PLX4032 cell line S. neurona (SN-37R) tachyzoites were maintained under the same conditions by the continuous passage of HeLa cells and CV-1 cells, respectively, at 37 °C and 5% CO2 in RPMI medium supplemented with 25 mM HEPES, 2 mM of l-glutamine, 3 mM sodium bicarbonate and antibiotic/antimycotic solution (penicillin 100 IU/mL, streptomycin 100 μg/mL and amphotericin B 0.25 g/mL; Gibco). T. gondii (RH) tachyzoites were maintained in BALB/c mice by serial passage for 48–72 h ( Mineo et al., 1980). This maintained licensed by the Ethics and Animal Experimentation Federal University of Uberlandia, with number 029/2012. A parasite suspension was washed two times (720 × g, 10 min, 4 °C) in phosphate-buffered saline 0.01 M (PBS, pH 7.2), treated with protease inhibitors (Complete, Roche) and then subjected to ten freeze–thaw cycles and sonication Oxygenase (60 Hz,

90% amplitude, in ice bath). After centrifugation (10,000 × g, 30 min, 4 °C), the supernatant was collected and filtered through 0.22 μm membrane (Millex, Millipore, USA). The supernatant, containing soluble antigens of N. caninum (NLA), S. neurona (SnLA) or T. gondii (STAg), was collected and the protein concentration was estimated using the Bradford assay. Aliquots were stored at −20 °C until use. Indirect ELISAs were carried out to detect IgG antibodies as described elsewhere Silva et al. (2007), with modifications. In summary, high-binding microtiter plates were coated with NLA, SnLA or STAg (10 μg/ml) in 0.06 M carbonate buffer (pH 9.6) overnight at 4 °C. The plates were then washed three times with PBS containing 0.

There is an additional population of neurons in the supramammillary region and extending laterally to selleck compound the subthalamic nucleus, which is

a known source of projections to the cerebral cortex and basal forebrain (Grove, 1988 and Saper, 1985). Many neurons in this region express the vesicular glutamate transporter 2 (Hur and Zaborszky, 2005 and Ziegler et al., 2002) but whether these glutamatergic neurons promote arousal remains to be determined. The most rostral population of arousal-promoting subcortical neurons is located in the basal forebrain. Many of these neurons contain either acetylcholine or gamma-amino-butyric acid (GABA), and a small number contain glutamate ( Manns et al., 2001 and Hur and Zaborszky, 2005). Basal forebrain cholinergic neurons innervate, both directly and indirectly activate cortical Neratinib pyramidal cells, and probably augment cortical activation and EEG desynchronization ( Jones, 2004). GABAergic basal forebrain neurons innervate and presumably inhibit cortical GABAergic interneurons and deep layer pyramidal cells ( Freund and Meskenaite, 1992 and Henny and Jones, 2008), both of which most likely result in disinhibition of cortical circuits.

Many of these basal forebrain neurons are wake-active and fire in bursts correlated with specific EEG rhythms. Small ibotenic acid lesions of the basal forebrain result in modest slowing of the EEG without changing the amount of wake or sleep, while specific lesions of basal forebrain cholinergic neurons reduce wakefulness transiently, without affecting the EEG frequency spectrum ( Kaur et al., 2008). On the other hand, acute inactivation Phosphatidylinositol diacylglycerol-lyase of the basal forebrain with the anesthetic procaine produces deep NREM sleep, whereas activation with glutamatergic agonists causes wakefulness ( Cape and Jones, 2000). A definitive understanding of the roles of the basal forebrain cell groups in arousal awaits studies that differentially eliminate the GABAergic population. The thalamic relay nuclei (such

as the anterior, ventral, and lateral thalamic cell groups; medial and lateral geniculate nuclei; mediodorsal nucleus; and pulvinar) are the most important and abundant sources of subcortical glutamatergic afferents to the cerebral cortex, and the intralaminar and midline nuclei provide a diffuse source of cortical input ( Jones and Leavitt, 1974). Surprisingly, there is little evidence that these inputs play a major role in producing wakefulness. Early electrical stimulation studies suggested that the midline and intralaminar thalamic nuclei might constitute a diffuse, nonspecific cortical activating system ( Morison and Dempsey, 1942 and Steriade, 1995), but lesions of the midline and intralaminar nuclei did not prevent cortical activation ( Moruzzi and Magoun, 1949 and Starzl et al., 1951).