99, −0 3, 0 4, 0 7, 0 95, and 0 999 The observable correlation t

99, −0.3, 0.4, 0.7, 0.95, and 0.999. The observable correlation through sampling by the subject will, however, very on a continuous Carfilzomib scale also between these steps due to Stochasticity in the outcomes. A change from the current to a new correlation was determined probabilistically in every trial with a p = 0.3 transition probability, under the constraint that a change would only occur after the new correlation became theoretically detectable by an ideal observer that was tracking the correlation coefficient in a sliding window over the past five trials. In detail, after the normatively estimated correlation based on the last five

trials (similar to the sliding window model below) approached the new generative correlation (with a deviation <0.2), the correlation was allowed to change on all further trials. This prevented overly rapid changes in the generative correlation before subjects could have possibly detected the new correlation coefficient from outcome observations. On average (across subjects and sessions) the correlations Selleck Smad inhibitor changed every ten trials. To discourage subjects from persevering on a more favorable spot of the response scale that would give a reasonable result over

a wider range of correlations, and instead be forced to track the correlation explicitly, we further implemented an adaptive rule that if subjects’ response was both suboptimal (farther from the optimum than 0.2) and they did not change their response within the past five trials then the correlation would jump to the farthest extreme (either −0.99 or +0.999). This increased the penalty on subjects payout at their current weights and encouraged them to find a better weight allocation. In practice, this constraint came rarely (never for 10 subjects, one or two occurrences in five, and three occurrences in one subject) into use during the fMRI experiment. We modeled trial-by-trial values

of the correlation strength by using principles of reinforcement learning (Sutton and Barto, 1998). Reinforcement learning Dichloromethane dehalogenase generates in every trial a prediction error as the deviation of the experienced outcome R from the predicted outcome. Those prediction errors, multiplied by the learning rate, are then used to update predictions in future trials: equation(1) resourcevalue:Vi,t+1=Vi,t+αVδi,t,and equation(2) valuepredictionerror:δi,t=R−Vi,t. The squared prediction error is also a measure of the outcome fluctuation and thereby a quantifier of risk. A sequence of continuously large prediction errors indicates that the outcomes greatly fluctuate, whereby a sequence of small prediction errors indicate that prediction is precise with little deviation. We used this to model the risk h for both resources: equation(3) resourcevariance:hi,t+1=hi,t+αRεi,t,and equation(4) variancepredictionerror:εi,t=δi,t2−hi,t.

It could be the synaptic mechanism behind the cross-modal suppres

It could be the synaptic mechanism behind the cross-modal suppressive interactions shown with extracellular recordings

in ferrets (Bizley et al., 2007) and macaques (Kayser et al., 2008 and Lakatos et al., 2007). Interestingly, cross-modal deactivations have been described also in human occipital cortex using neuroimaging (Laurienti et al., 2002). Albeit we give evidence that the majority of V1 neurons are inhibited by sound, we also found that this is due to acoustic-driven excitation of few infragranular cells. This observation is consistent with other reports of spiking responses driven by heteromodal stimuli in primary sensory areas ( Bizley et al., 2007, Morrell, 1972 and Wallace et al., 2004). In line with our findings, such responses are mostly restricted to deep cortical laminae in rodents ( Wallace et al., 2004). buy PF-02341066 Long-range recruitment of inhibitory subcircuits could be a way to control the fluctuations of subthreshold neural activity in early sensory cortices (Cardin et al., 2009 and Traub et al., 1996), and therefore their phase of excitability. In fact, cross-modal click here modulation of responsiveness in early cortices depends on stimulus onset asynchrony,

indicating a time-dependent modulation of cortical excitability induced by heteromodal stimulation (Lakatos et al., 2007). This type of interaction plays a key role in sensory coding, since cross-modal modulation of oscillatory activity in early sensory areas is supposed to add information about external stimuli (Kayser et al., 2010)

by providing a time reference to spikes. SHs resetted the phase of ongoing V1 activity and were often followed by a depolarization of the cell. Interestingly, when visual stimuli were presented during the depolarizing plateau, visual responsiveness increased (G.I. and P.M., unpublished data). The GABAergic silencing of local network activity driven by heteromodal stimuli could be the condition allowing the phase-resetting of ongoing activity observed extracellularly by our and other groups (Lakatos et al., 2007). What is the functional significance of SHs in V1? First, the fact that activation of a primary cortex by a salient stimulus (such as a noise burst in A1) degrades neuronal before processing in neighboring areas is in line with the idea that sensory cortices compete for the activation of higher cortical areas. The steep emergence of SHs with increasing sound intensities suggests that, for interareal inhibition to be effective, a certain threshold of activation of A1 has to be reached, particularly to affect the animal’s behavior. The fact that SHs were evoked robustly for intensity larger than 55–60 dB SPL is in line with the view that an acoustic stimulus has to be salient for this mechanism to be recruited. Second, it is tempting to speculate that heteromodal inhibition could modulate the selectivity of visual cortical neurons for stimulus attributes such as orientation.

For example, CaMKIIβ mRNA is strikingly restricted to the soma an

For example, CaMKIIβ mRNA is strikingly restricted to the soma and proximal dendrites (Martone et al., 1996) and its protein expression

is dynamically regulated in homeostatic plasticity (Thiagarajan et al., 2002). AMPA-induced AMPAR internalization also occurs primarily in soma and proximal dendrites of hippocampal neurons (Biou et al., 2008). Together, these findings suggest a role of the proximal dendrite as a homeostatic domain and also emphasize the importance of specifying the dendritic subregion studied in homeostatic plasticity. This caveat may explain, at least in part, discrepancies in the literature regarding AMPAR subunits involved in homeostatic regulation. The function of Plk2 in vivo has been GSI-IX explored previously using knockout animals, but not examined in synaptic plasticity (Inglis et al., 2009 and Ma et al., 2003a). We used a dominant-negative transgenic approach to address potential functional redundancy by binding to all shared targets of the Plk subfamily and inactivating them by sequestration. However, it is highly likely that Plk2 is the relevant polo family kinase involved in activity-dependent homeostatic synaptic downregulation, based on the similar effect of Plk2 RNAi on spines in cultured neurons to DN-Plk2 expressed in TG animals, the absence of Plk1 and Plk4 expression in normal brain tissue (Winkles and Alberts,

2005), and lack of effect of Plk3 RNAi on PTX-induced homeostatic plasticity in any of our assays. The precise function of Plk3 in brain is unknown, selleckchem but as this kinase was originally identified as an FGF-inducible of factor, Plk3 may be responsive to neurotrophic or other growth factor stimulation. DN-Plk2 animals exhibited increases in RasGRF1 and SPAR protein levels, similar to expression of DN-Plk2 in dissociated neuron culture. These effects were accompanied by elevated levels

of active Ras and several phenotypes consistent with previously described consequences of Ras overactivity including increased ERK activation, slightly enlarged cortex (probably due to neuronal hypertrophy) (Heumann et al., 2000), higher spine density (Arendt et al., 2004), and elevated GluA1 expression (Kim et al., 2003). Perhaps the most striking observation was that TG forebrains had nearly undetectable levels of active Rap1 or Rap2. Thus, Plk2 appears to be critically required for Rap activation in the brain, at least under basal conditions of normal ongoing activity. It is nevertheless probable that Plk2-independent pathways to Ras and Rap regulation exist, particularly under conditions of acute plasticity or stimulation (Woolfrey et al., 2009). Ras signaling plays critical roles in learning and memory (Mazzucchelli and Brambilla, 2000). Somewhat surprisingly, DN-Plk2 mice exhibited normal working memory and no deficit in acquisition of the Morris water maze task.

, 1997 and Vinogradov et al , 2008) Furthermore, we demonstrate

, 1997 and Vinogradov et al., 2008). Furthermore, we demonstrate that training-induced enhancement of neural activation patterns associated with reality monitoring predict subsequent improvement in longer-term social functioning. This study also addresses the fundamental issue of whether “brain training”

improves cognitive functions beyond the PS-341 order trained tasks (Owen et al., 2010). Schizophrenia is a serious and debilitating psychiatric illness that affects 51 million people worldwide. Affected individuals experience a range of disturbing clinical symptoms indicating a break with reality—such as hallucinations and delusions—as well as a range of neurocognitive and social cognitive deficits (Cirillo and Seidman, 2003 and Heinrichs and Zakzanis, 1998). Prominent among these deficits are impairments in memory, executive function, and in the assessment of social cues such as facial emotion (Chan et al., 2010, Glahn et al., 2000 and Silver et al., 2007). Pharmacologic treatment of schizophrenia targets symptom reduction, but the neurocognitive and social cognitive impairments, which are not improved by current medications, are more predictive of poor functional outcome

than are the clinical symptoms of hallucinations and delusions (Evans et al., 2004 and Green et al., 2000). Despite an understanding of the see more strong association between cognitive impairment and long-term disability no in patients, the treatment of schizophrenia is at a stalemate (Carter and Barch, 2007 and Marder and Fenton, 2004). New cognitive-enhancing medications studied thus far have been disappointing, and conventional psychotherapeutic and psychosocial rehabilitation approaches have been of limited benefit, likely due to the cognitive limitations of the illness (Green et al., 2008, Pilling et al., 2002 and Smith et al., 2010). Informed by the past two decades of systems neuroscience research into the learning mechanisms that drive sustained plastic changes in the cortex (Buonomano

and Merzenich, 1998, Jenkins et al., 1990, Karni and Sagi, 1991 and Merzenich et al., 1990), we predicted that—in order to improve higher order cognitive functions in human neuropsychiatric illness—computerized training must be designed to intensively target impairments in lower-level perceptual processing as well as working memory and executive operations (Adcock et al., 2009, Fisher et al., 2009, Mahncke et al., 2006 and Vinogradov et al., 2012). In other words, training must initially target lower-level processes in order to increase the accuracy, the temporal and spatial resolution, and the signal strength of auditory and visual inputs to working memory and executive functions, ultimately increasing the efficiency of more complex, higher-level cognitive processes in an enduring manner (Vinogradov et al., 2012).

5 to 18 7 ± 3 4 s (n =

6; Figure 1A) This effect of Rp-c

5 to 18.7 ± 3.4 s (n =

6; Figure 1A). This effect of Rp-cGMPS was concentration-dependent, with a 50% inhibition concentration (IC50) of 0.15 μM (see Figure S1 available online). This IC50 is 4 times higher than that estimated in biochemical assays (0.035 μM) (Butt et al., 1995), but ∼50 times lower than that estimated for the vasodilatation assay (7.2 μM) (Taylor et al., 2004). Another type of PKG-specific inhibitor KT5823 (10 μM) also slowed endocytic τ0.5 to 19.8 ± 3.0 s (n = 6; Figure 1A) like Rp-cGMPS (3 μM). Neither Rp-cGMPS nor KT5823 affected ΔCm or ICa (Figure 1A). As Rp-cGMPS is membrane permeable, preincubation of slices with check details Rp-cGMPS (3 μM) for 1 hr at room temperature (RT) also slowed endocytic τ0.5 to 18.8 ± 2.9 s (n = 3, data not shown), similar to its effect after direct loading into calyces (Figure 1A). These results

suggest that endogenous PKG normally upregulates the endocytic rate of synaptic vesicles at mature calyces of Held. In contrast to PKG inhibitors, the PKA inhibitor KT5720 (1 μM), loaded into calyces, had no effect on the time course of vesicle endocytosis induced by a 20 ms depolarizing pulse (data not PCI32765 shown). However, it has recently been reported that preincubation of slices of P9–P11 rats with KT5720 (2 μM) slows endocytic capacitance change, more effectively after longer pulse depolarization (Yao and Sakaba, 2012). It remains to be seen whether the slowing effect of the PKA inhibitor on endocytosis persists after hearing onset. We further examined whether the PKG-dependent speeding of vesicle endocytosis operates before hearing. At calyces before hearing onset (P7–P9), endocytosis elicited with our standard

stimulation protocol was much slower (τ0.5, 17.2 ± 2.0 s, n = 6; Figure 1B) than that at P13–P14 calyces, but similar to that in Rp-cGMPS-loaded P13-P14 calyces (Figure 1B). Furthermore, in contrast to P13–P14 calyces, direct loading of Rp-cGMPS (3 μM) into P7–P9 calyces had no effect on endocytic time course (τ0.5, 17.3 ± 2.7 s, n = 5). These results suggest that at calyceal synapses after hearing onset, a PKG-dependent mechanism supports the high rate of endocytosis. At the calyx of Held, vesicle endocytosis undergoes developmental speeding (Renden and von Gersdorff, 2007 and Yamashita et al., Tolmetin 2010). Our results suggest that maturation of the PKG-dependent mechanism underlies this development. At many synapses, including immature rodent calyces before hearing, the time required for endocytosis is proportional to the amount of exocytosis (von Gersdorff and Matthews, 1994, Wu and Betz, 1996, Sun et al., 2002, Yamashita et al., 2005 and Balaji et al., 2008). However, at calyces of Held after hearing, the endocytic time constant becomes constant and independent of the magnitude of exocytosis (Renden and von Gersdorff, 2007 and Yamashita et al., 2010).

This question was addressed by assessing the effects of early lif

This question was addressed by assessing the effects of early life FGF2, administered the day after birth, on emotionality, hippocampal development Panobinostat concentration and gene expression (Turner et al., 2011). Remarkably, a single injection of FGF2 (20 ng/g, subcutaneously) early in life was able to alter neurogenesis in outbred animals. In adulthood, these animals exhibited a denser dentate gyrus with more

neurons, consistent with the idea that neurogenesis precedes gliogenesis in early development (Palmer et al., 1999). Moreover, when the same early life FGF2 treatment was given to high anxiety animals (bLRs), FGF2 decreased their spontaneous anxiety (Turner et al., 2011). This effect was associated with altered gene expression in the dentate gyrus. Laser capture microdissection followed by microarray analyses identified transcripts that differed between bLR-VEH and bLR-FGF2 animals. Specifically, molecules previously associated with anxiety (gad1) were decreased, whereas molecules associated with cell

survival (bcl2-like2) were increased in the high anxiety bred rats in conjunction with decreased anxiety by FGF2 treatment. Thus, early life FGF2 treatment altered the developmental trajectory of the dentate gyrus and had long-term effects on emotionality and gene expression. Most recently, a study by Duman’s group extended these findings to mice and to other models of stress (Elsayed et al., 2012). Thus, the authors reported Selleckchem Docetaxel that chronic infusion of FGF2 had antidepressant-like effects in both rats and mice. They also added site-specificity to the antidepressant effects by infusing FGF2 into the medial prefrontal cortex. Moreover, FGF2 blocked the effects of

chronic unpredictable stress (CUS) on both Metalloexopeptidase depression-like behavior, and the CUS-induced inhibition of glial proliferation. Treatment with an FGF receptor antagonist that targets all FGF receptors blocked the effects of fluoxetine on glial proliferation, as well as the effect of fluoxetine as an antidepressant. These results suggest that not only is FGF2 a sufficient antidepressant, it is also necessary for the antidepressant effects of SSRIs, although the lack of selectivity of the available FGF antagonists requires caution in the interpretation of these latter results. Moreover, the study by Elsayed et al. (2012) also hinted at relatively rapid effects of FGF2 in animal models of depression and anxiety (5 days after administration). We have also observed rapid effects of FGF2 in other paradigms. Indeed, some of the behavior and biochemical effects of FGF2 can be observed within minutes and certainly within hours, but the mechanism of these rapid effects needs further exploration. FGFR1 is required for the electrophysiological correlate of learning and memory, long-term potentiation (Zhao et al., 2007).

, 2006) It would be useful to have a standard measure that is

, 2006). It would be useful to have a standard measure that is

as brief as possible and has proven validity. This paper reports on the validation of such a measure using a large population sample. Three published studies have examined associations between measures of motivation to quit and quit attempts prospectively in population samples in the absence of interventions (Borland et al., 2010, West et al., 2001 and Zhou et al., 2009). Many other studies have examined the predictive validity of measures of motivation to stop in clinical samples or in the context of interventions studies (for example: Biener and Abrams, 1991, Boardman et al., 2005, Crittenden et al., 1994, Hughes et al., Ipatasertib ic50 2005, Ong et al., 2005 and Sciamanna et al., 2000). Others have examined the predictive value of measures of “stage of change” which incorporates past quitting behavior and so conflates motivation and previous action (Cancer Prevention Research Center, 2012 and DiClemente et al., 1991). It also represents a very broad classification in pre-quit stages and has been found to have low temporal stability (Hughes

et al., 2005). For the purposes of evaluating a standard scale for population samples, reports of associations in clinical and intervention studies cannot be used. The three relevant prospective studies found moderate associations between measured motivation and subsequent quit attempts but no attempt learn more was made to define a function relating scores on the measures and the behavioral outcome (Borland et al., 2010, West et al., 2001 and Zhou et al., 2009). Key elements of motivation include beliefs about what one should do, and both desire and intention to act in a particular way (West, 2005). In relation to motivation to stop smoking, it has been found that intention oxyclozanide and desire to stop are predictive of quit attempts while belief alone that one should stop is not (Smit et al., 2011). A simple rating scale has been constructed that incorporates all of these components: the Motivation To Stop Scale (MTSS). This scale was developed for use in large scale

tracking surveys by RW in collaboration with the English Department of Health and Central Office of Information. It should provide an ordinal measure of motivation to stop smoking which would allow assessment of all the relevant aspects of motivation. It is important to note that this rating specifically includes intention, desire and belief into a single item with the expectation that this will provide the most cost-efficient possible measure. Splitting the constructs into two or three items would double the cost and for large surveys this could represent a substantial decrease in cost-efficiency. This study assessed the predictive validity of the MTSS by examining associations between scores on the scale and incidence of attempts to stop smoking in the subsequent 6 months.

The data suggest that nAChRs and mAChRs may be localized to diffe

The data suggest that nAChRs and mAChRs may be localized to different populations of GABAergic terminals, but from these studies, it is difficult to determine what the effects of synaptically

evoked ACh on LH GABA release might be. Optogenetic stimulation of cholinergic transmission in the LH and hypothalamus will be useful in identifying the source of ACh input to these areas, the role of intrinsic ACh in hypothalamic function, and the differential role of mAChRs and nAChRs in shaping responses to ACh in these brain regions. In the arcuate nucleus of the hypothalamus, nicotine increases the firing rate of both POMC- and neuropeptide Y (NPY)-positive neurons, although the increase in POMC neuron activity predominates in vitro due ABT199 to more rapid desensitization of nAChR responses in NPY neurons, and in vivo, as evidenced by an increase in c-fos immunoreactivity

predominantly in POMC-positive cells ( Huang et al., 2011; Mineur et al., 2011). Thus, as in the mesolimbic system and the cortex, distinct actions of ACh appear to converge through effects on receptor populations with different electrophysiological properties expressed on distinct subsets of neurons to promote a coordinated output, in this case, activation of POMC neurons. ACh also regulates glutamatergic transmission in other neuronal subtypes involved in food intake. Stimulation of nAChRs on orexin-positive neurons in the LH induces concurrent release of glutamate and ACh, which could lead Selleck VE822 to feed-forward stimulation

of this circuit once activated (Pasumarthi and Fadel, 2010). There is also some indication from studies of hypothalamic neurons in culture that ACh signaling can be upregulated to compensate for prolonged blockade of glutamatergic signaling (Belousov et al., 2001). Thus, ACh acting through nAChRs may also potentiate glutamate signaling in particular neuronal subtypes of the hypothalamus, although the functional consequences of this regulation are not yet known. As might be expected from the complex regulation of hypothalamic neuronal activity by ACh, Adenosine cholinergic modulation of feeding behavior is multifactorial and state-dependent. In rats, the mAChR competitive antagonist atropine modestly altered the frequency and choice of meals, but not their size (Nissenbaum and Sclafani, 1988). Consistent with the ability of nicotine in tobacco smoke to decrease body weight in humans and food intake in rats (Grunberg et al., 1988), β4-containing nAChRs on POMC neurons are critical for the ability of nicotine to reduce food intake in mice (Mineur et al., 2011). These observations underscore a potential role for ACh in metabolic regulation involving POMC neurons; however, very little is known about the role of endogenous ACh-mediated modulation of the arcuate nucleus.

Evidence from human fMRI studies has been consistent with this hy

Evidence from human fMRI studies has been consistent with this hypothesis, demonstrating that novelty at encoding elicits responses in SN/VTA that are associated with beneficial effects on subsequent memory (Wittmann et al., 2007; Schott et al., 2004; Krebs et al., 2009). Importantly, as noted above, SN/VTA cells also provide dopaminergic input into the striatum (Figure 1B) where the information they convey about expected reward and other behaviorally relevant features of an input, like novelty, can influence learning, action selection, and decision-making. For example, when harvesting reward in a stochastic environment, strategically directing

exploratory behavior to novel items has the potential to glean the most new information about that environment (Kakade and Dayan, selleck 2002; Daw et al., 2006; Frank et al., 2009; Badre et al., 2012). Indeed, striatal novelty responses have been specifically associated with novelty-driven choices during economic decisions (Guitart-Masip et al., 2010; Wittmann et al., 2008; Krebs et al., 2009). Moreover, many studies citing SN/VTA activation in response to novelty, also report responses to novel greater than familiar items in the striatum (e.g., Bunzeck and Düzel, 2006; Guitart-Masip et al., 2010). Notably, AZD8055 ic50 these activations fall in close proximity to those associated

with retrieval success (Figure 2). Thus, considered together with retrieval success effects, the evidence for novelty responses in the striatum argues against obligatory coding of item oldness in striatum as a consequence of episodic retrieval. Rather, striatal responses to episodic memory signals are likely modulated depending on the adaptive significance of “oldness” or “newness” to the animal’s current actions and Phosphatidylinositol diacylglycerol-lyase desired outcomes. Two recent findings provide support for this hypothesis.

Bunzeck et al. (2010) showed that responses in the striatum are scaled adaptively based on expectations of the relative novelty and oldness of items in the environment. Han et al. (2010) more directly manipulated the goal relevance of item novelty versus oldness and revealed a similar dynamic flexibility in striatal responses. Specifically, retrieval goals were manipulated by associating either “old” or “new” responses with potential monetary reward. When “old” responses were incentivized, participants earned money for correct old responses (hits) and lost money for incorrect old responses (false alarm) and neither gained nor lost money for “new” responses (and vice versa when “new” was incentivized). Activity in the caudate tracked the incentivized response independent of whether the item was studied or novel (Figure 3).

, 2008, Fliegauf et al , 2007, Lancaster and Gleeson, 2009, Sharm

, 2008, Fliegauf et al., 2007, Lancaster and Gleeson, 2009, Sharma et al., 2008, Sloboda and Rosenbaum, 2007 and Veland et al., 2009). In this review, we focus more narrowly on ciliopathic symptoms that direct attention to cilia-dependent aspects of neural development and function. Ciliopathies showing a strong association with neural defects include

Joubert Syndrome, Bardet-Biedl Syndrome (BBS), and Alström Syndrome. Joubert Syndrome is a phenotypically and genetically heterogeneous group of disorders whose defining features are hindbrain defects, and related neurological symptoms such as breathing abnormalities, ataxia, and developmental delay. Joubert Syndrome can also be associated with hydrocephalus, anatomical abnormalities in the cerebral cortex, autism spectrum disorders, and retinal dystrophy. Bardet-Biedl Syndrome (BBS), also genetically heterogeneous, is marked by cognitive Cisplatin disabilities, anosmia, obesity, and retinal degeneration. Obeticholic Acid purchase Alström Syndrome, caused by mutations in the ALMS1 gene, is associated with obesity and retinal degeneration ( Table 2)

(http://www.ncbi.nlm.nih.gov/omim). The specific associations of cilia with these abnormalities will be discussed further below. The third major program of research discloses one reason why primary cilia appear on neural progenitor cells, and illustrates the primary cilium as an organelle specialized to receive an environmental signal. In this case, the signal is the secreted molecule Shh, which specifies neuronal cell type in the ventral neural tube, and configures Isotretinoin digits in the limb bud, as well as patterning other structures in the embryo (Chiang et al., 1996, Echelard et al., 1993, Ericson et al., 1995 and Roelink et al., 1995). Evidence linking Shh signaling to cilia came from a program of forward genetics in mice that screened for neural tube defects

and dorsoventral patterning abnormalities. This screen generated mutants with phenotypes similar to those caused by disruptions in Shh signaling (Huangfu et al., 2003), yet the mutations were not in genes encoding Shh signaling components, but those encoding IFT proteins and the ciliary kinesin and dynein motor proteins (Table 1). Core players of the Hedgehog (Hh) pathway in the mouse include Shh, the transmembrane proteins Smoothened (Smo) and Patched 1 (Ptch1), and the transcription factors Gli2 and Gli3. Importantly, these and additional pathway components, including Kif7 (the vertebrate homolog of Drosophila Costal 2 [Cos2], a hub for Hh signaling in the fly) and suppressor of Fused (Sufu, a negative regulator of the pathway) have been localized to cilia in vertebrates ( Table 3), and live imaging will be critical for testing in future the current models of their trafficking within the cilium. In essence, Shh signaling regulates the balance between Gli transcriptional activators and repressors in a manner appropriate to the tissue being patterned.