, 2009, Guarraci and Kapp, 1999 and Matsumoto and Hikosaka, 2009). Since

the neurons with excitatory responses to aversive events were excited by rewarding selleck chemical events as well, they were presumed to encode motivational salience rather than motivational value (Matsumoto and Hikosaka, 2009). Based on these findings, it was proposed that dopamine neurons are not a homogeneous population and are divided into multiple groups encoding distinct signals suitable for different functions (Bromberg-Martin et al., 2010b). Consistent with the idea, the dopamine system is involved in multiple functions. Especially, dopamine released in the prefrontal cortex (PFC) has been implicated in cognitive processing rather than motivational functions (Nieoullon, 2002 and Robbins and Arnsten, 2009), including attentional selection (Crofts et al., 2001 and Robbins and Roberts, 2007), saccade target selection (Noudoost and Moore, 2011), and performance monitoring (Ullsperger, 2010 and Vezoli and Procyk, 2009). In particular, a prominent role in working memory has been established. Extracellular dopamine level increases in the dorsolateral prefrontal cortex (dlPFC) during working memory performance (Watanabe et al., 1997), and the blockade of dopamine D1 receptors in the dlPFC impairs working memory (Li and Mei,

1994, Sawaguchi and Goldman-Rakic, 1991 and Sawaguchi and Goldman-Rakic, 1994). An electrophysiological study in PD184352 (CI-1040) monkeys performing spatial working memory tasks also reported consistent data showing that the blockade of dopamine D1 receptors attenuates the spatially tuned persistent firing BMN 673 of dlPFC neurons (Williams and Goldman-Rakic, 1995). Dopamine is therefore essential to prefrontal cognitive functions. These findings have inspired hypotheses about what signals dopamine neurons might convey to the PFC to support these cognitive functions (Cohen et al., 2002 and Durstewitz et al., 2000). However, despite the wealth of studies demonstrating that dopamine neuron signals are related to reinforcement

and motivation, little is known about whether dopamine neurons convey signals suitable for promoting cognitive processing. In the present study, we aimed at identifying the signals carried by dopamine neurons when monkeys were engaged in a cognitive task. Specifically, we recorded single-unit activity from dopamine neurons in the ventral midbrain, including the SNc and VTA, while monkeys were performing a delayed matching-to-sample (DMS) task that required working memory and visual search. We found that the activity of dopamine neurons at different locations within the ventral midbrain reflected signals suitable for distinct roles in cognitive processing. We trained two monkeys (monkey F and monkey E) to perform a DMS task (Figure 1A). Each trial began with the presentation of a colored fixation point.

To determine whether GDE2 is required for the generation of motor neurons of distinct subtypes, we examined motor column formation in WT and Gde2 null littermates ( Figure S3; Tsuchida et al., 1994, Rousso et al., 2008 and Dasen et al., 2008). At fore- and hindlimb levels, Gde2 null animals showed an approximately 40%–50%

loss of medial and lateral LMC neurons at E11.5 and a decrease of 30%–35% at E13.5 ( Figures 3A–3G and 3O–3U), whereas we noted a modest decrease of 20% in thoracic HMC neurons at E11.5 ( Figures 3H–3N). Strikingly, no changes in the numbers of MMC neurons or PGC neurons were found at either time point ( Figures 3A–3U). The loss of HMC and LMC neurons in Gde2−/− animals is unlikely to be due to impaired Hox activities because of the following: (1) gain or loss of Hox gene function does not reduce motor neuron numbers; (2) expression see more of FoxP1, a critical cofactor of Hox function,

in existing motor neurons is unaffected by the loss Lonafarnib research buy of GDE2 ( Figures 3A–3T; Rousso et al., 2008 and Dasen et al., 2008); and (3) brachial Hoxc6, thoracic Hoxc9, and lumbar Hoxa10 expression are preserved in motor neurons of Gde2−/− animals ( Figure S3; Jung et al., 2010). V2 interneurons derive from Lhx3+ progenitors, and V2 interneuron differentiation programs are actively suppressed in motor neurons by the transcription factor HB9 ( Arber et al., 1999, Thaler et al., 1999 and Thaler et al., 2002). Islet1/2 motor neurons did not coexpress Chx10, no increases in cell death by TUNEL were detected, and V2 interneuron numbers were unchanged in the absence of GDE2, arguing against the possible conversion of prospective HMC and LMC neurons to V2 fates ( Figures S2 and S3; data not shown). Taken together, these observations suggest that GDE2 function is restricted to the formation of LMC and HMC motor neurons and invokes the existence of other regulatory modules that control the formation of GDE2-independent motor neurons. The loss of fore- and hindlimb LMC neurons in Gde2−/− animals indicates that GDE2 activity is not restricted to a specific rostral-caudal domain, whereas the partial reduction

of medial and lateral LMC neurons suggests that GDE2 might be required for the formation of distinct LMC motor pools ( Figure 3). We analyzed Gde2 null L-NAME HCl animals at lumbosacral segment (LS) 2 of the spinal cord, where combined molecular and axonal tracing approaches have defined a molecular code that distinguishes seven medial and lateral LMC motor pools that innervate major muscle groups in the hindlimb ( Figure 4C; De Marco Garcia and Jessell, 2008, Lin et al., 1998 and Arber et al., 2000). These include five motor pools within the medial LMC that innervate the adductor longus and magnus (Al, Am), the adductor brevis (Ab), and the anterior and posterior gracilis muscles (Ga, Gp), as well as two lateral LMC pools that target the vasti (Va) and the rectofemoratibialis muscles (Rf).

The relationship between increases in VMPFC activation and subsequent inference performance was present even when equating for differences in memory for directly learned associations (partial r = 0.53, p = 0.007; p < 0.05 Bonferroni corrected). The relationship between hippocampal activation decreases and inference performance was only significant in right hippocampus when accounting for performance on directly learned associations (bilateral hippocampus partial r = 0.22, p = 0.29; right hippocampus partial r = 0.39, p = 0.05). No other brain region demonstrated a significant

relationship between changes in activation (increases or decreases) across AB repetitions when controlling for performance on directly learned associations, though Ponatinib price a statistical trend was observed in inferior frontal gyrus pars orbitalis (r = 0.38, p = 0.06). These findings indicate that the relationship between right hippocampal and VMPFC encoding activation and subsequent inference goes above and beyond learning of directly experienced associations, suggesting

that these regions mediate binding www.selleckchem.com/products/scr7.html of current experiences to reactivated memories. In line with recent rodent research (Iordanova et al., 2007 and Iordanova et al., 2011; Tse et al., 2007 and Tse et al., 2011), the present findings indicate that hippocampus and VMPFC are both engaged in support of retrieval-mediated learning. To further test for learning-related changes in hippocampal-VMPFC coupling, we performed

a functional connectivity analysis using bilateral hippocampus as the seed region to determine whether the pattern of connectivity between hippocampus and VMPFC changed across repeated presentations of overlapping associations. Within each individual functional run, we constructed separate regressors corresponding to the first, second, and third repetitions of individual associations for each participant. A repeated-measures ANOVA revealed that hippocampal-VMPFC connectivity increased across repetitions of overlapping associations irrespective of the functional run (repetition linear trend F(1,21) = 9.78, p = 0.005). Importantly, hippocampal-VMPFC connectivity did not change over the course of the experiment (run linear trend F < 1); rather, increases in hippocampal-VMPFC connectivity were specific to repetitions of from overlapping events within each run (repetition x run interaction F(1,21) = 1.74, p = 0.20; Figure 6), suggesting increased functional connectivity between hippocampus and VMPFC during the online formation of integrated memory representations. Three additional regions—frontal pole, precuneus, and superior parietal cortex—showed increased connectivity with hippocampus across repetitions of overlapping associations (Figure S4); however, unlike VMPFC, encoding activation in these regions was not related to inference performance (all r < 0.14, p > 0.5).

A constant current isolated stimulator (Digitimer, Welwyn Garden City, Hertfordshire, UK) delivered continuous electrical pulses to the STN electrodes at an intensity below the threshold for induced movement (50–250 μA). The motor performance of the hemi-Parkinsonian rat before (5 min), during (2 min), and after (5 min) STN-DBS were compared with the spontaneous exploratory movement (5 min) of intact rats (ANY-maze 4.70 software; Stoelting,Wood Dale, IL). The dependence of the efficacies of DBS-STN on stimulation frequencies (0.2, 1, 5, 10, 50, 125, 200, and 250 Hz) and pulse width (10, 20, 40, 60,

80, and 100 μs) were studied systematically. While the animals were performing in the open field test, both extracellular AZD2014 nmr spike trains and the local field potentials (LFPs) in MI were recorded simultaneously using a 32-channel electrophysiological data acquisition system (OmniPlex system, Plexon, Dallas, TX). In the behavioral assessment, muscle contractions in the contralateral face and limb could be induced when the stimulation site was located at the lateral STN border (confirmed

postmortem) or the stimulation amplitude used was high (>1 mA). Thus, contralateral muscle contraction at low threshold stimulation was indicative of the possibility that the electrode was very near or inserted CP-690550 ic50 into the internal capsule and considered unacceptable. For all other cases, the stimulation value was set below the threshold of visible muscular contraction, but at which it could bring behavioral improvement. The t tests were performed to compare the motor performance from different groups. Paired t tests were performed on the data

from hemi-Parkinsonian 17-DMAG (Alvespimycin) HCl rats only, comparing the STN-DBS period to both the “pre” and “post” periods. To study the dependence of behavioral improvement on stimulation frequency and pulse width in hemi-Parkinsonian rats, an additional ANOVA repeated-measures analysis (stimulus frequency and pulse width as repeated-measures, respectively) followed by a LSD post hoc test was also performed. All these behavioral test results are shown as mean ± SEM. The stimulus artifact removal and single-unit spike-sorting process were performed in the Off-line Spike Sorter V3 workspace (Plexon, Dallas, TX), using a combination of automatic and manual sorting techniques. Burst discharge was quantified by the Legendy surprise method. Cross-correlation analysis was applied to study the synchronization level among CxFn. The oscillatory rhythm in MI was measured as the spectrum of LFP using fast Fourier transform at 0.2 Hz resolution. When investigating the coherence phase between the spikes of each CxFn and the simultaneously recorded LFP, the polar histogram was built by filtering the LFP into beta band (11–30 Hz). Coronal sections were cut at the STN (20 μm), MI (20 μm), SNc (20 μm), and striatum (200 μm) by freezing microtome.

These findings reveal unexpectedly that despite the fact that activation of the BAD-BAX-caspase-3 pathway usually leads to cell death, neurons adopt this entire pathway for induction of LTD, and underscore the importance of quantitative differences in caspase-3 activation

for determining the cellular function of this pathway. To determine the mechanism for caspase-3 activation in LTD, we first examined whether knocking selleck compound down the expression of BAD, BAX and BID would affect LTD, as these proteins activate caspase-3 in apoptosis. To this end, we generated constructs expressing siRNAs that target the mRNAs encoding these proteins. The efficiency and specificity of these siRNAs were tested against

corresponding cDNAs expressed in heterologous cells and against their endogenous targets in cultured hippocampal or cortical neurons. As shown in Figure S1 available online, the siRNAs were highly effective and specific. To test their effect on synaptic transmission, we biolistically transfected cultured hippocampal slices with the siRNA constructs Obeticholic Acid along with a plasmid expressing venus (a YFP mutant) (Nagai et al., 2002) and measured excitatory postsynaptic currents (EPSCs) evoked by stimulating the Schaffer collateral pathway. As shown in Figures 1A–1D and Table S1, the amplitudes of both AMPA and NMDA receptor-mediated currents (EPSCAMPA and EPSCNMDA, respectively) were comparable in untransfected cells and in cells transfected with control or siRNA plasmids. These results indicate that NMDA receptor functions and basal AMPA receptor-mediated currents are intact in the transfected cells. We then proceeded to test the effect of siRNAs on NMDA receptor-dependent LTD induced by a pairing low-frequency stimulation protocol (see Experimental Procedures). LTD was blocked by the selective NMDA receptor antagonist APV [(2R)-amino-5-phosphonovaleric acid](data not shown), confirming that this

stimulation protocol induces NMDA receptor-dependent LTD. Simultaneous whole-cell recordings were conducted in pairs of transfected and nearby untransfected CA1 neurons in Megestrol Acetate the same slice. As shown in Figure 1E, LTD as revealed by a reduction of EPSCs measured 30 min after stimulation was comparable in untransfected and control plasmid transfected cells (56 ± 9% of baseline [preinduction] in untransfected cells; 49 ± 6% of baseline in control plasmid transfected cells; p = 0.52, n = 11 pairs; Figure 1E). Similarly, LTD was not altered in BID siRNA transfected cells (62 ± 6% of baseline in untransfected neurons; 61 ± 8% of baseline in BID siRNA transfected neurons; p = 0.92, n = 10 pairs; Figure 1F).

A domain from RalGDS selectively binds RAP1-GTP; a domain from c-Raf binds Let-60-GTP (de Rooij and Bos, 1997 and Franke et al., 1997). RAP-1-GDP and LET-60-GDP are not bound. GSH-Sepharose 4B beads (Pharmacia) containing 5 μg of bound GST-RalGDS-RBD or GST-Raf-RBD were added to clarified lysates. After incubation at 4°C for 3 hr,

beads were isolated by centrifugation at 10,000 × g for 10 min. After 4 washes in Ral buffer, isolated proteins were analyzed by western immunoblot assays. Assays were performed on 10 cm Petri plates (Bargmann and Horvitz, 1991). Attractant (1 μl) and 1 μl of ethanol (neutral control) were applied to the agar at opposite ends of the plate (0.5 cm from the edge). NaN3 (1 mM) was added to attractant and ethanol to immobilize animals that reached the reservoirs. Animals (150) were placed at the center of the plate. After LDK378 datasheet 2 hr at 20°C, numbers of animals clustered at attractant (A) and ethanol (C) reservoirs were counted. A chemotaxis selleck chemical index (CI) was calculated: CI = (A − C)/(A + C + worms elsewhere). The maximum chemotaxis value is +1.0. CI values were measured on triplicate plates and averaged. Experiments

performed with BZ, BU, or IAA yielded similar results. Representative data, obtained using one, two, or all three attractants, are presented. Characterization of RGEF-1a and RGEF-1b cDNAs; preparation of transgenes, expression vectors, and transgenic animals; mutagenesis, DNA, protein, and qR-PCR analyses; characterization of an rgef-1 gene deletion; antibody production, intracellular targeting of RGEF-1b-GFP; immunofluorescence microscopy; and the MPK-1 activation assay are described in Supplemental Experimental Procedures. This work was supported by NIH grants GM080615 (C.S.R.) and T32 HL007675 (L.C.). We thank Erik Snapp, Dave Hall, and Zeynep Altun for reagents, discussions, and advice. “
“The delivery, removal, and recycling of surface Mephenoxalone membrane proteins through cytoskeletal transport regulates a variety of cellular processes including cell adhesion and cellular signaling in various cell types (Hirokawa and Takemura, 2005 and Soldati and Schliwa,

2006). Because of their polar and excitable nature, neurons represent cells with special requirements for transport. For instance, the rapid turnover of neurotransmitter receptors to and from postsynaptic membranes controls synaptic plasticity, the ability of individual synapses to change in strength (Kennedy and Ehlers, 2006 and Nicoll and Schmitz, 2005). Cytoskeletal transport is powered by molecular motor complexes that shuttle cargoes to specific subcellular compartments. A growing number of transport complexes have been functionally described in neurons (Caviston and Holzbaur, 2006, Hirokawa and Takemura, 2005 and Soldati and Schliwa, 2006). However, the question of how cargo is guided across different cytoskeletal tracks to reach distinct subcellular destinations remains unanswered.