Therefore, WCs are not truly novel to the mothers (Ehret and Bernecker, 1986). In contrast, adult mice normally do not hear USVs prior to their experience AZD5363 supplier with the pups as parents. As a result, primiparous females are first exposed to their pup USVs in the context of their body odors. This novel combination may promote

high acuity to this specific, context-dependent combination of stimuli contingent with stressed pups. It is well established that the auditory cortex can discriminate sounds that acquire behavioral meaning (Fritz et al., 2003 and Weinberger, 2004). In line with these classical forms of experience-dependent plasticity, the percentage of units responding to USVs was higher (relative to that in naive virgins) in all experimental groups that had previous interaction with the pups (Figure 6B). These changes were seemingly independent of pup odors and may well be a result of the change in acoustic environment related to the presence of pups (i.e., USVs). Both the odor-dependent and the odor-independent changes promote higher detection levels of USVs (Figures 6B–6D) and possibly better discrimination by the mother. Whether these changes follow a mechanism of classical association learning between sounds and smells remains to be seen. As a general observation, we show

that pup odor induced modulation of sound detectability. In particular, the representation of USVs in A1 increased. What may be the neural mechanism underlying this long-term change in A1? Neurons in A1 (as in any neocortical circuit)

process Bumetanide information Dasatinib molecular weight differently across layers (Harris et al., 2011 and Sakata and Harris, 2009). Thus, one may expect that the long-term changes in sensory responses would have unique signatures in different layers and interactions therein. Unexpectedly, we did not observe any particular pattern of change based on the depth of our neuronal recordings (not in spontaneous or in evoked firing and not in the odor-evoked changes; analyses not shown). Notably, the lack of layer specificity may still be a limitation of our recording method, which yields relatively low numbers of neurons from each layer in our data set. Dense recording techniques or imaging techniques may be a more informative way to measure odor-induced effects across layers (Happel et al., 2010, Rothschild et al., 2010 and Sakata and Harris, 2009). Pup odors affected the excitatory responses of all cells with no particular reference to their spontaneous or sound-evoked spike rates (Figures 5A and 5B; Figures S1–S3). However, modulation did not affect all neuronal cell types in the same manner. The majority of FSNs showed consistent changes in the form of an increase in their sound detectability (Figure 5B). Moreover, FSNs had a higher probability to respond to sounds compared to RSNs (19/28 versus 132/270). Could FSNs be central to the mechanism of change? Emerging data in the field suggest that they may. FSNs are the major source of inhibition onto RSNs (i.e.

, 2001). Interestingly, task-induced DMN deactivation was shown to have a neuronal origin ( Lin et al., 2011), so it may relate to intrinsic

inhibitory properties of local cortical circuits. Few studies have focused on differences in deactivation in ASD, but our findings are highly consistent with those of Kennedy et al. (2006), who reported that individuals with ASD exhibit less deactivation within regions of the DMN. The auditory cortex is also known to deactivate during visual tasks ( Laurienti et al., 2002; Mozolic et al., 2008), and in our study the auditory cortex exhibited the strongest deactivation differences between genotype groups during this visual task. These findings of reduced deactivation of perisylvian and DMN regions in MET learn more risk carriers may relate to a failure to appropriately suppress neuronal activity, perhaps through an enhancement of local connectivity that was influenced by MET during development, as reported in the Met mutant mouse by Qiu et al. (2011). Future imaging and neurophysiological studies are needed to test this hypothesis. The fact that MET risk carriers displayed

altered DMN deactivation patterns further prompted us to test whether the risk allele impacts intrinsic functional connectivity in this network, particularly since DMN connectivity has consistently been shown to be disrupted in ASD ( Cherkassky et al., 2006; Kennedy and Courchesne, 2008; Monk et al., 2009; Weng et al., 17-AAG research buy 2010; Assaf et al., 2010; Rudie et al., 2012). Indeed,

we found that MET risk carriers and individuals with ASD exhibited reductions in long- as well as short-range DMN connectivity. The combination of reduced deactivation and connectivity supports the notion that the DMN is both less integrated with itself and less segregated second from other neural systems in both MET risk carriers and individuals with ASD ( Rudie et al., 2012). Additionally, these findings suggest that functional alterations in the DMN represent a trait marker shared in those with, or at risk for, ASD. Future work should characterize functional connectivity alterations in other networks as a function of the MET risk allele. Next, we examined whether structural connectivity was altered in MET risk carriers, as the MET protein is highly expressed during axon outgrowth in specific WM tracts in primates ( Judson et al., 2011a). Remarkably, the presence of the MET risk allele was associated with much stronger disruptions in WM integrity than having an ASD diagnosis. The effects were most pronounced in temporo-parietal regions of high MET expression and especially within the splenium, which includes fiber pathways originating from the posterior cingulate/precuneus of the DMN. This hub region, implicated in all three imaging analyses, has been characterized as the structural core of the human connectome ( Hagmann et al., 2008).

Measures of altered hormone are reported in chronic migraine patients (including prolactin, cortisol, and melatonin), which is indicative of abnormalities

in circadian biology (Peres et al., 2001). Thus, the hypothalamus may control systems that could have many functional implications through such alterations in hormone and autonomic function, impinging on many organ systems, including the brain. One example is that of the association of obesity and migraine (Peterlin et al., 2010). Alterations in hypothalamic control click here may be manifest and contribute to both syndromes, because alterations in neurotransmitters and hypothalamic peptides may be abnormal in both conditions. Given that there may be multiple stressors that contribute to the allostatic load (Figure 4) and increased disease burden with chronification Epacadostat in vivo (Figure 5), ideally, one could evaluate and quantify each and provide a rational approach to devolving, uncoupling, diminishing direct inputs onto systems that modulate the allostatic load and directly impact those systems that have been altered. A new approach to defining and measuring the relative contributions

and their cumulative or additive effects would bring opportunities to improve diseases such as migraine where we only have a limited response in terms of preventing the attacks and/or treating chronic migraine. Specifically, the following principles would seem to be salient: (1) intervene as early as possible to prevent the negative cascade; (2) top-down interventions (e.g., exercise, social support, stress reduction, diet, etc. [see McEwen and Gianaros, 2011]) to help reestablish systemic and brain “balance,”

which may include plastic changes and neuronal connectivity (Castrén, 2005); (3) pharmacotherapy may contribute to the top-down process and may be more efficacious in the context of other modulators of allostasis. One example in support of interactive Histone demethylase effects is the use of antidepressants in the context of a positive therapeutic environment and the notion that multiple therapies may be more beneficial than a single treatment (March et al., 2004). Another example in support of our proposal is the efficacy of antidepressants, along with physiotherapy, in recovery of motor function after stroke (Chollet et al., 2011). (4) On the other hand, as noted above, some medications may contribute to the allostatic overload and make the condition worse. Thus, targeting treatments in the context of modification of multiple neurobiological systems would seem like a rational process to implement at a clinical level. Specific targets include behavioral targets (including sleep and stress modification), but also those directed at brain systems, as noted below.

Thus, the FEF seems to combine incoming feature information from V4 with working memory signals carrying information about the relevant features to compute a saliency map

that highlights the locations of potential targets. This map not only guides gaze but also provides feedback signals to V4 in order to enhance the processing of stimuli sharing the target color and/or shape (Figure 1A, bottom panel). Note that according to this hypothesis, although the trigger signal for the FEF saliency computation is a stimulus feature, the nature of the top-down signal is spatial, since it highlights LGK-974 in vivo locations of potential targets, i.e., it enhances responses of neurons with receptive fields that include stimuli resembling the target. A difference between this and the previously proposed feature-similarity mechanism of attentional modulation is that here the attentional enhancement occurs in neurons with receptive fields that include stimuli

matching the target features (feature matching or FM [Motter, 1994]), rather than in neurons selective for the attended stimulus feature across the entire visual field (feature-selectivity gain or FSG, [Treue and Martínez Trujillo, 1999]). The distinction between these two alternatives can be made by measuring tuning curves for the different shapes and colors in V4 and FEF neurons and then determining whether the attentional enhancement occurred mainly in neurons selective for the target color or shape (FSG) or in any neuron containing a stimulus that matches the target feature within its find more receptive field, independently of the unit’s selectivity DNA ligase for that feature (see Figure S6 of Zhou and Desimone [2011]). This distinguishes between a feature-based mechanism that combines feature and spatial information within a saliency map (FM) from another mechanism that combines information about the attended feature and the neurons selectivity (FSG). The study of Zhou and Desimone (2011) shows that neurons in the FEF are well suited to perform the computations underlying

FM, and that the results of these computations guide visual search. The details of how different signals are combined within the FEF microcircuitry remain to be determined. In a second study, also available in this issue of Neuron, Cohen and Maunsell (2011) implanted multielectrode arrays in V4 in both hemispheres of macaque monkeys and recorded the activity of single and multiple neurons during a task that required the deployment of spatial and feature-based attention. During the task, animals covertly attended to a stimulus at a cued fixed position in the visual field and detected a change in one of the stimulus features (orientation or spatial frequency). By introducing similar feature changes in a distracter stimulus presented simultaneously with the attended target and quantifying performance, the authors made sure that the animals correctly performed the task ( Figure 1B).

Sample traces are shown in Figure 7D, and frequency data from multiple recording are quantified check details in Figure 7E. As previously reported, syb2 KO neurons exhibit significantly decreased mIPSC frequency compared to littermate controls (Schoch et al., 2001). vti1a-pHluorin expression has no effect on mIPSC frequency in syb2 KO neurons, whereas ΔN vti1a-pHluorin expression

dramatically increases mIPSC frequency, similar to the results shown for wild-type neurons (Figures 6J and 6K). Next, we assessed the effect of vti1a KD in the absence of syb2. In Figure 7F, sample mIPSC traces of uninfected syb2 KO neurons or those infected with vti1a-1 KD or vti1a-3 KD are depicted. Although syb2 KO neurons exhibit reduced mIPSC frequency compared to wild-type neurons (see Figure 7E), KD of vti1a essentially abolished the remaining spontaneous neurotransmission seen in these neurons. Cumulative histogram data from multiple recordings are presented in Figure 7G and show significantly decreased mIPSC frequency in syb2 KO/vti1a KD neurons compared to uninfected syb2 KO neurons. vti1a KD did not affect average mIPSC amplitudes in the absence of syb2 (syb2 KO = 15.84 ± 1.57 pA, syb2 KO/vti1a-1 KD = 12.55 ± 0.99 pA, and syb2 KO/vti1a-3 KD = 16.22 ± 2.31 pA). While KO of syb2 impairs

selleckchem most evoked SV trafficking (Schoch et al., 2001), we show that the functional impact of vti1a (as judged by fluorescence imaging as below well as electrophysiology) is identical to its properties seen in wild-type

synapses. These findings argue for a direct executive function of vti1a in spontaneous release that is independent of syb2. At central synapses, syb2 is the predominant vesicular SNARE that ensures rapid execution and fidelity of fusion reactions (Schoch et al., 2001). However, loss-of-function studies of syb2 as well as other key SNAREs involved in fusion suggest that a parallel pathway, possibly involving noncanonical SNAREs typically implicated in constitutive vesicle trafficking, may mediate fusion and recycling of a subset of vesicles (Bronk et al., 2007, Deák et al., 2004, Schoch et al., 2001 and Washbourne et al., 2002). Recent observations that vesicles giving rise to evoked and spontaneous neurotransmitter release may originate from distinct pools (Chung et al., 2010, Fredj and Burrone, 2009 and Sara et al., 2005), taken together with the finding that this distinction is largely lost in syb2-deficient synapses (Sara et al., 2005), prompted us to survey noncanonical SNAREs shown to be resident on SVs (Takamori et al., 2006) that may selectively sustain spontaneous release. Fluorescence imaging experiments revealed that both vti1a and VAMP7 were capable of trafficking at rest. Vti1a, however, possessed a more prominent intracellular pool and more robust trafficking at rest compared to VAMP7.

Western blot analysis during development confirmed the continued absence of GluN2B and the premature expression of GluN2A in 2B→2A cortical neurons this website (see Figure S1 available online). RT-PCR analysis of mRNA harvested from P17 homozygous 2B→2A animals showed the absence of GluN2B transcript and replacement with exogenous rat mRNA encoding GluN2A, in addition to the endogenous mouse transcript (Figure 1E). Together, these data show that the genetic strategy was successful and predict that in homozygous 2B→2A animals, glutamatergic cortical synapses contain only

GluN2A-containing receptors within a GluN2B null background. Genetic deletion of GluN2B leads to perinatal lethality (Kutsuwada et al., http://www.selleckchem.com/products/BAY-73-4506.html 1996). 2B→2A mice also displayed a high rate of perinatal lethality, indicating that premature expression of GluN2A is not sufficient to fully rescue GluN2B loss of function. Genotyping embryos harvested to prepare cortical cultures showed that the transmission frequency of the 2B→2A allele followed a predicted pattern with an approximately 1:2:1 (WT:HET:2A→2B) ratio from matings between heterozygous 2B→2A animals (19:41:21, in ten cultures).

However, only ≈8% of 2A→2B homozygous animals survived past P0: 6 out of 72 predicted animals (24 months following removal of the neo cassette, data from 38 litters). Surviving homozygous 2B→2A pups exhibited no differences in size at P0 but were significantly stunted in for mass when measured as juveniles (P12–P16) ( Figures 1G and 1H). In homozygous 2B→2A pups at P0, we recorded a decrease in the number of rhythmic mouth suckling movements in response

to stimulation with a feeding needle ( Figure 1I), suggestive of a weakened ability to feed. This is consistent with an observed lack of milk in their bellies at this age ( Figure 1G) and is similar to the GluN2B full knockout animals, which show a complete absence of suckling behavior. These observations underscore the importance of NMDAR signaling during development but also suggest a required and specific role for GluN2B-containing NMDARs. To determine whether or not GluN2A-containing receptors were properly expressed and trafficked to the neuronal membrane in 2B→2A mice, we generated neuronal cultures from homozygous embryos as well as WT embryos at E16–E17 and performed immunofluorescence analysis of GluN1. GluN1 is the obligate subunit of the NMDAR, and thus its expression in dendrites will correlate positively with total receptor levels. We measured similar levels of anti-GluN1 staining in 2B→2A dendrites compared to WT controls by costaining with the dendritic marker MAP2 (Figure 2A). To examine surface-localized protein, we used a live-labeling protocol. This analysis showed that the amount of membrane-associated GluN1 protein in 2B→2A neurons was comparable to WT (Figure 2B).

LGN response PSTHs were modeled as half-wave rectified sinusoids, and scaled to the model cell’s firing rate on each trial. The postsynaptic conductance change evoked by each LGN cell was modified by synaptic depression as measured experimentally (Boudreau and Ferster, 2005). Synaptic efficacy depended on input firing rate (computed in 12.5 ms bins), reaching

an asymptote at 70% BMN673 of the original value at high input rates: equation(Equation 5) Efficacy(t)=0.7+0.3e−rate(t)/25Efficacy(t)=0.7+0.3e−rate(t)/25 Evoked conductance depressed to ∼90%, ∼75%, and ∼70% of its nondepressed value at LGN firing rates of 20 Hz, 50 Hz, and 100 Hz. The summed input evoked a depolarization according to Equation 1 and Equation 2. The simple cell was modeled as a point neuron in steady-state, i.e., conductance changes were assumed to occur on a time scale slower than the membrane time constant. No active conductances or inhibitory inputs were included. We are grateful to Dr. Kenneth D. Miller, Dr. Mark

M. Churchland, and Dr. Nicholas J. Priebe for many insightful comments and suggestions on the manuscript and Jianing Yu and Hirofumi Ozeki for helpful discussions. This work was supported by NIH grant R01 EY04726 to D.F. “
“The neural signature of visual consciousness can be detected in the electrical activity of multiple cortical areas across the visual hierarchy, during tasks that permit a dissociation of purely sensory stimulation from subjective perception. Binocular rivalry (BR) and binocular flash suppression (BFS) are extensively used paradigms of such ambiguous stimulation in which two disparate visual patterns, presented at corresponding parts of the two retinas, compete for RAD001 research buy access to perceptual second awareness. Electrophysiological recordings combined with BR and/or BFS showed a stronger correlation between conscious visual perception and neuronal activity in higher association areas of the cortex. In the primary visual cortex (V1) and visual area V2, only 14%

of the recorded sites and 20%–25% of single units fired more when a preferred stimulus was consciously perceived (Gail et al., 2004, Keliris et al., 2010 and Leopold and Logothetis, 1996). In cortical areas V4 and MT, single unit activity (SUA) was also weakly correlated with perceptual dominance since only 25% of the recorded population was found to discharge in consonance with the perceptual dominance of a preferred stimulus (Leopold and Logothetis, 1996, Logothetis and Schall, 1989 and Maier et al., 2007). Interestingly, V4 and MT showed significant traces of nonconscious stimulus processing since a fraction of the perceptually modulated selective neurons (13% and 20%, respectively) fired more when their preferred stimulus was perceptually suppressed. In striking contrast, almost 90% of the recorded units in the superior temporal sulcus (STS) and inferior temporal (IT) cortex reflected the phenomenal perception of a preferred stimulus (Sheinberg and Logothetis, 1997).

CNO was obtained from the NIH as part of the Rapid Access to Investigative Drug Program funded by the NINDS. This work was supported

by grants from “Anonymous Foundation” and NARSAD to C.K., from the International Mental Health Research Organization, the Hope for Depression Research Foundation, and the NIH (R21 MH093887 and R01 MH081968) to J.A.G, U19MH82441 to B.L.R., and a Fondation Fyssen Fellowship to S.P. A.I.A. is a Leon Levy Foundation Resident Fellow and is supported by R25 MH086466. S.P. designed and performed the experiments, conducted the data analysis, and wrote the paper. P.K.O. performed the in vivo recordings experiment and conducted the analysis. S.S.B. and R.D.W. helped with behavior experiments. A.I.A. assisted in the analysis of the in vivo recordings. B.L.R. provided the DREADD system. P.B. supervised and designed behavioral IOX1 concentration experiments. J.A.G. and C.K. designed and supervised the performance of the experiments and data analysis and wrote the paper. “
“New experiences are accompanied by profound increases in the level of coordinated

memory reactivation in the hippocampus during sharp-wave ripple (SWR) events (Foster and Wilson, 2006; Cheng and Frank, 2008; Karlsson and Frank, 2008; O’Neill et al., 2008). These reactivation events frequently replay entire behavioral trajectories representing either past or possible future locations (Foster and Wilson, 2006; Diba and Buzsáki, 2007; Davidson et al., 2009; Karlsson and Frank, 2009; Gupta et al., 2010) and reactivation strength during and after an experience correlates with subsequent memory (Nakashiba selleck screening library et al., 2009; Dupret et al., 2010). Disrupting SWRs during sleep leads to subsequent performance deficits in a spatial memory task (Girardeau et al., 2009; Ego-Stengel and Wilson, 2010), and disrupting SWRs during behavior causes performance

deficits in a spatial learning task (Jadhav et al., 2012). While these findings have established the importance of SWRs for learning, it is unclear how SWR activity contributes to memory-guided behavior. We have hypothesized that SWR reactivation represents recent and possible future paths to aid ongoing memory-guided navigation (Karlsson and Frank, 2009; almost Carr et al., 2011). However, to date no one has examined whether reactivation during learning is related to choice behavior in a hippocampally dependent spatial task. We asked how SWR reactivation could aid memory-guided decisions in animals learning a W-track alternation task in initially novel environments (Frank et al., 2000; Karlsson and Frank, 2008; Kim and Frank, 2009). We focused on the outbound, SWR-dependent component of the task (Jadhav et al., 2012). On outbound trials, animals begin in the center arm of the track. Correct performance of the task is to alternate between outside arms. To accomplish this, animals must remember which outside arm they visited most recently and choose a path to the opposite arm.

24, p < 0.05). Whether or not individual PPC cells passed the criterion for showing tuning to acceleration or self-motion could not be predicted by the cluster isolation of the cells (Z-scores for large-sample binomial comparisons of PPC cells with isolation scores above and below the median isolation distance ranged from 0.66 to 1.76, p values ranged from 0.08 to 0.51). To determine whether PPC cells exhibit anticipatory firing, as described in primates prior to eye or hand movements

(see Andersen and Buneo, 2002 for review), we analyzed whether the time window within which PPC cells showed self-motion tuning extended to include movements from path segments that preceded Selleckchem Perifosine or succeeded the animal’s actual position (Figure 4). The cells showed tuning to upcoming actions that occurred up

to 500 ms after the spikes. The tuning of the cells fell off almost immediately BAY 73-4506 research buy after a movement was executed, suggesting that the tuning was genuinely anticipatory and not related to the temporal structure of the animal’s movements (Figures 4B and 4C). Thus, PPC cells in rats express information about ongoing and impending movements during unrestrained foraging, whereas grid-cell maps are independent of the state of motion. Since PPC cells showed tuning to self-motion and acceleration in the open field, we reasoned that spatial correlates may emerge when particular behaviors are executed reliably at particular not locations (see also McNaughton et al., 1989 and McNaughton et al., 1994). To determine this we recorded from the same rats as in the open field in a hairpin maze comprised of a stack of 10 interconnected, equally

sized alleys running north-to-south (Figure 5A) (Derdikman et al., 2009). The maze was constructed by inserting opaque Perspex walls in to floor grooves in the open field arena, allowing us to maintain the same recording location and spatial cues outside the arena. The rats were trained to make repeated east-to-west and west-to-east traversals during 20 min recording sessions, receiving a food reward at the end of each lap. The maze limited the rats’ modes of movement to sequences of straight running, left turns, and right turns, causing the emergence of apparently-spatial firing fields. Cells that preferred straight running fired in maze alleys, while other cells fired just before or after turns, and other cells fired during the turns themselves (Figure 5B). The firing fields were stable within and between recording sessions and the discharge correlates were the same for east- and westbound trajectories (Figure 5B). Simultaneously recorded grid cells also expressed discrete firing fields in the hairpin maze (Figure 5C).